Dino Ienco
email: dino.ienco@irstea.fr

Raffaele Gaetano
email: raffaele.gaetano@cirad.fr

Claire Dupaquier
email: claire.dupaquier@irstea.fr

Pierre Maurel
email: pierre.maurel@irstea.fr

Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks

Keywords: Recurrent Neural Networks, Satellite Image time series, Land Cover classification, Deep Learning

Nowadays, modern earth observation programs produce huge volumes of satellite images time series (SITS) that can be useful to monitor geographical areas through time. How to efficiently analyze such kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification (i.e. Convolutional Neural Networks -CNNs -on single images) while only very few studies exist involving temporal deep learning approaches (i.e Recurrent Neural Networks -RNNs) to deal with remote sensing time series.

In this letter we evaluate the ability of Recurrent Neural Networks, in particular the Long-Short Term Memory (LSTM) model, to perform land cover classification considering multi-temporal spatial data derived from a time series of satellite images. We carried out experiments on two different datasets considering both pixel-based and object-based classification. The obtained results show that Recurrent Neural Networks are competitive compared to state-of-the-art classifiers, and may outperform classical approaches in presence of low represented and/or highly mixed classes. We also show that using the alternative feature representation generated by LSTM can improve the performances of standard classifiers.

I. INTRODUCTION

M ODERN earth observation programs produce huge volumes of remotely sensed data every day. Such information can be organized in time series of satellite images that can be useful to monitor geographical zones through time. Efficiently manage and analyze remote sensing time series is still an open challenge in the remote sensing field [START_REF] Karpatne | Monitoring land-cover changes: A machine-learning perspective[END_REF].

In the context of land cover classification, exploiting time series of satellite images, instead that one single image, can be fruitful to distinguish among classes based on the fact they have different temporal profiles [START_REF] Abade | Comparative analysis of modis time-series classification using support vector machines and methods based upon distance and similarity measures in the brazilian cerrado-caatinga boundary[END_REF]. Despite the usefulness of temporal trends that can be derived from remote sensing time series, most of the proposed strategies [START_REF] Flamary | Analysis of multitemporal classification techniques for forecasting image time series[END_REF], [START_REF] Heine | Classification and monitoring of reed belts using dual-polarimetric terrasar-x time series[END_REF] directly apply standard machine learning approaches (i.e. Random Forest, SVM) on the stacked images. Since these approaches did not model temporal correlations, they manage features independently from each others, ignoring any temporal dependency which data may exhibit. Recently, the deep learning revolution [START_REF] Zhang | Deep learning for remote sensing data: A technical tutorial on the state of the art[END_REF] has shown that neural network models are well adapted tools to manage and automatically classify remote sensing data. While standard CNNs techniques are well suited to deal with spatial autocorrelation, the same approaches are not adapted to correctly manage long and complex temporal dependencies [START_REF] Bengio | Representation learning: A review and new perspectives[END_REF]. A family of deep learning methods especially tailored to cope with temporal correlations are Recurrent Neural Networks [START_REF] Bengio | Representation learning: A review and new perspectives[END_REF] and, in particular, Long Short Term Memory (LSTM) networks [START_REF] Greff | LSTM: A search space odyssey[END_REF]. Such models explicitly capture temporal correlations by recursion and they have already proved to be effective in different domains such as speech recognition [START_REF] Graves | Speech recognition with deep recurrent neural networks[END_REF], natural language processing [START_REF] Linzen | Assessing the ability of lstms to learn syntax-sensitive dependencies[END_REF], image completion [START_REF] Van Den Oord | Conditional image generation with pixelcnn decoders[END_REF]. Only recently, in the remote sensing field, the work proposed in [START_REF] Lyu | Learning a transferable change rule from a recurrent neural network for land cover change detection[END_REF] performs preliminary experiments with LSTM model on a (small) time series composed of only two dates to perform supervised change detection. The task was modeled as a binary classification problem (change vs. no-change). To the best of our knowledge, RNNs (i.e. LSTM) have not yet been considered to deal with land cover classification of deeper time series. Like any other deep learning model [START_REF] Bengio | Representation learning: A review and new perspectives[END_REF], LSTM can be used as a classifier itself or employed to extract new discriminative features (or representation). In the latter case the extracted features are successively used to feed a standard learning algorithm that does not consider temporal dependencies (i.e. Random Forest, Naive Bayes, KNN, SVMs).

In this letter we evaluate the quality of RNN models -Long Short Term Memory -to deal with land cover classification via multi-temporal spatial data that are derived from SITS. More in detail, we perform experiments on two study areas: i) the THAU basin, a site located in the south of France, from which we obtain a time series of 3 dates, and ii) the REUNION ISLAND, a region of France located in the Indian Ocean (east of Madagascar) from which we derive a 23-date times series. While on the first area we conduct an object-oriented classification, on the second site we have performed a pixel-based prediction showing the general applicability of RNNs models to both object and pixel-level analysis. We also assess the RNN model (i.e. LSTM) as feature extractor evaluating the quality of the new generated features to feed the same baseline classifiers we have used as reference methods.

The rest of the paper is organized as follows: Section II introduces the LSTM unit and specifies the network architecture we propose, Section III describes the two datasets, the time series characteristics and the preprocessing we have performed on the source data. Experimental setting and results are discussed in Section IV. Conclusions are drawn in Section V.

II. METHOD

We propose a neural architecture involving LSTM unit to deal with land cover classification via multi-temporal spatial data. We also assess the representation learned by the RNN model with standard classification strategies commonly used to perform prediction in the remote sensing field.

A. Long-Short Term Memory

Recurrent Neural Networks are well established machine learning techniques that demonstrate their quality in different domains such as speech recognition [START_REF] Graves | Speech recognition with deep recurrent neural networks[END_REF], signal processing [START_REF] Soma | Simultaneous multichannel signal transfers via chaos in a recurrent neural network[END_REF], natural language processing [START_REF] Linzen | Assessing the ability of lstms to learn syntax-sensitive dependencies[END_REF] and image completion [START_REF] Van Den Oord | Conditional image generation with pixelcnn decoders[END_REF]. Differently from standard feed forward networks (i.e. CNNs), RNNs explicitly manage temporal data dependencies since the output of the neuron at time t-1 is used, together with the next input, to feed the neuron itself at time t. A sketch of a typical RNN neuron is depicted in Figure 1. The most well-known type of RNN is the Long-Short Term Memory (LSTM) [START_REF] Greff | LSTM: A search space odyssey[END_REF] model. There are many variants of LSTM network [START_REF] Greff | LSTM: A search space odyssey[END_REF] but here we refer to the architecture proposed in [START_REF] Gers | Learning to forget: Continual prediction with LSTM[END_REF]. LSTM models were mainly introduced with the purpose to learn long term dependencies [START_REF] Greff | LSTM: A search space odyssey[END_REF], since previous RNN models failed in this task due to the problem of vanishing and exploding gradients. The equations ( 1), ( 2), ( 3), ( 4), ( 5) and ( 6) formally describes the LSTM neuron while Figure 2 graphically depicts the LSTM unit. The symbol indicates an element-wise multiplication while σ and tanh represent Sigmoid and Hyperbolic Tangent respectively. The input of the LSTM is a sequence of variables (x 1 , ..., x N ) where a generic element x t is a feature vector and t refers to the corresponding timestamp. RNN models are able to manage variable-length data sequences.

The LSTM unit is composed of two cell states, the memory C t and the hidden state h t , and three different gates, the input gate i t , the forget gate f t and the output gate o t that are employed to control the flow of information. All the three gates combine the current input x t with the hidden state h t-1 coming from the previous timestamp. The gates have also two important functions: i) they regulate how much information have to be forgotten/reminded during the process; ii) they deal with the problem of vanishing/exploding gradients. We can observe that the gates are implemented by a sigmoid. This function returns values between 0 and 1 where, in this context, 0 indicates that the information is completely forgotten and 1 means that the information is completely retained.

The LSTM unit uses also a temporary cell state y t that rescales the current input always taking into account the previous hidden state. This temporary cell is implemented by an hyperbolic tangent function that returns values between -1 and 1. Both sigmoid and hyperbolic tangent are applied element-wise.

The input gate i t regulates how much of the current information needs to be maintained (i t y t ) while the forget gate f t indicates how much of the previous memory needs to be retained at the current step (f t c t-1 ). Finally, the output gate impacts on the new hidden state h t deciding how much information of the current memory will be outputted to the next step. The different W * * matrices and bias coefficients b * are the parameters learned during the training of the model. Both, the memory C t and the hidden state h t are forwarded to the next time step.

i t = σ(W ix x t + W ih h t-1 + b i ) (1) f t = σ(W f x x t + W f h h t-1 + b f ) (2) y t = tanh(W yx x t + W yh h t-1 + b y ) (3) c t = i t y t + f t c t-1 (4) o t = σ(W ox x t + W oh h t-1 + b o ) (5) h t = o t tanh(c t ) (6)

B. LSTM-Based Time Series Classification

The LSTM neuron learns an internal representation of the input sequences (in our case objects or pixels time series) but it does not make any prediction by itself. To perform the classification task, we stack on top of the LSTM neuron a SoftMax layer [START_REF] Graves | Speech recognition with deep recurrent neural networks[END_REF] to accomplish the final multi-class prediction. The SoftMax layer has as many neurons as the number of the classes to predict. We choose SoftMax instead of Sigmoid function because the value of the SoftMax layer can be seen as a probability distribution over the classes that sum to 1 while each of the Sigmoid neurons can output a value between 0 and 1. This is due to the fact that, for the SoftMax neuron, the values are normalized per layer while no normalization is performed in the case of Sigmoid layer. This is why, in our context (multi-class prediction), we prefer the SoftMax instead of Sigmoid layer since we know that our samples exclusively belong to a single class. From an architectural point of view, the connection between the LSTM and the SoftMax layer is realized fully connecting the last hidden state vector produced by the LSTM unit with the SoftMax neurons.

C. Representation Learning with LSTM for time series data

Standard deep learning approaches can also be seen as a way to produce a new, more discriminative representation of the original data [START_REF] Bengio | Representation learning: A review and new perspectives[END_REF]. Another way to assess the quality of LSTM unit for our land cover classification task is to use the features learned by the LSTM layer to feed a standard classifier. More in detail, we propose to employ the last hidden state vector, produced by the LSTM unit, as new data representation and, successively, train standard machine learning classifiers over such new set of features.

III. DATA

In order to prove the generality of our proposal, it has been tested over two different remote-sensing based datasets. The first is a collection of spatial objects described by a set regional statistics extracted from very high spatial resolution imagery (VHSR), but with a limited temporal depth. The second one is a pixel-based dataset, more noisy but richer in both spectral and temporal resolution. Detailed descriptions are provided in the following subsections.

A. THAU dataset

The first dataset has been generated using a time series of Pléiades VHSR images (2 m) acquired in the context of the Airbus DS/Spot Image distribution (July and September 2012, March 2013, c CNES). The study site is the THAU Basin located in the South of France, close to Montpellier. It covers an area of 42 000 ha with 70% of land area. The north is mainly composed of agricultural fields (i.e. vineyards) and natural spaces while the south is dominated by urban and industrial zones. For each date, two orthorectified, atmospherically corrected scenes are mosaicked.

Using the multi-temporal stack, a segmentation has been performed to extract a consistent multi-temporal object layer. Segmentation was performed using the Multiresolution Segmentation technique [START_REF] Baatz | Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation[END_REF] available in the eCognition Developer software. Each object has been then featured using statistical mean and standard deviation using the four native bands (blue, green, red and near-infrared) and the NDVI. A total of 10 features are computed per object and per date (5 means and 5 standard deviations).

The so obtained segments have been subsequently filtered and labeled in 11 different classes by visual inspection. A total of 15 196 objects is retained. The set of classes with the relative cardinality is reported in Table I

B. REUNION ISLAND dataset

The second dataset has been generated from an annual time series of 23 Landsat 8 images acquired in 2014 above the Reunion Island (2866 × 2633 pixels at 30 m spatial resolution), provided at level 2A1 . Source data have been further processed to fill cloudy observations via pixel-wise multi-temporal linear interpolation on each multi-spectral band (OLI) independently, and compute complementary radiometric indices (NDVI, NDWI and brightness index -BI). A total of 10 features (7 surface reflectances plus 3 indices) are considered for each pixel at each timestamp.

Reference land cover data has been built using two publicly available dataset, namely the 2012 Corine Land Cover (CLC) map and the 2014 farmers' graphical land parcel registration (Régistre Parcellaire Graphique -RPG). The most significant classes for the study area have been retained, and a spatial processing (aided by photo-interpretation) has also been performed to ensure consistency with image geometry. Finally, a pixel-based random sampling of this dataset has been applied to provide an almost balanced ground truth. The final reference dataset consists of a total of 37 900 pixels distributed over 9 classes as reported in Table II

IV. EXPERIMENTAL RESULTS

In this section we report the experimental settings and we discuss the results we obtained on the two SITS datasets we presented in Section III.

A. Experimental Settings

We compare the LSTM-based Time Series Classification model to standard machine learning approaches commonly employed to perform land cover classification from multi-temporal spatial data [START_REF] Flamary | Analysis of multitemporal classification techniques for forecasting image time series[END_REF], [START_REF] Heine | Classification and monitoring of reed belts using dual-polarimetric terrasar-x time series[END_REF]. We also assess the value of the representation learned by the proposed model following the idea described in Section II-C.

To our purpose, we use Random Forest (RF) and Support Vector Machine (SVM) as standard classification strategies. For the RF model, we set the number of generated trees equals to 400 and we allow a maximum tree depth of 10. For the SVM model we use RBF kernel with complexity parameter and gamma equal to 100 and 0.01 respectively. For Random Forest we used the python implementation supplied by the Scikit-learn library [START_REF] Pedregosa | Scikit-learn: Machine learning in Python[END_REF] while for SVM we use the LibSVM implementation [START_REF] Chang | LIBSVM: A library for support vector machines[END_REF]. The same RF and SVM settings are used for both original data and the new representation learned by the LSTM-Based Time Series Classification model. For the latter, we set the number of hidden dimensions equal to 512, an initial learning rate equals to 5 × 10 -4 and a decay of 5 × 10 -5 . We implement the model via the Keras python library [START_REF] Chollet | keras[END_REF] with Theano as back end. We used as optimization method the RMSprop strategy that is commonly employed to train LSTM units [START_REF] Dauphin | Rmsprop and equilibrated adaptive learning rates for non-convex optimization[END_REF]. The model is trained for 200 epochs with a batch size equals to 20. We named RF(LSTM) (resp. SVM(LSTM)) the Random Forest (resp. SVM) learned over the new feature space induced by our RNN model. More in detail, each training and test instance is transformed in a 512 feature vector (the dimension of the hidden state of the LSTM neuron) and, successively, the classifiers are learned from this new representation instead of the original data.

To validate the different methods, we perform a 5-fold cross validation. Due to the unbalanced nature of the two time series datasets, in order to assess classification performances we use not only the Global Accuracy and Kappa measures, but we also provide average and per-class F-Measure.

B. Results and Discussions

The Tables III and IV and Figures 3 and4 summarize the results we have obtained on the two SITS datasets. Considering the THAU dataset, Table III depicts the average values of Accuracy, F-Measure and Kappa for the different methods. We can observe that the LSTM-based classifier outperforms both RF and SVM approaches regarding all the three metrics. The more important gain is reached when the average F-Measure is taken into account, the LSTM-based classifier obtaining a score of 74.63% while the second best method (SVM(LSTM)) attains a score equals to 73.31%. Interestingly, we can also highlight that, for the THAU dataset, the classifiers trained on the features (representation) learned by the Recurrent Neural Network RF(LSTM) and SVM(LSTM) exhibit better performances than same classifiers coupled with the original time series data when the F-Measure is considered. In terms of Accuracy, the behavior is comparable considering the SVM vs SVM(LSTM) while the RF model clearly benefit of the new data representation.

A more detailed assessment is provided in Figure 3 where the per-class F-Measure is reported. The first point we can highlight is that, for classes with few reference samples (i.e. ( 1),( 4),( 8), ( 9) and ( 10)), the LSTM network neatly outperforms standard approaches, which in some cases (i.e. ( 1) and ( 4)) completely miss the classes, while it obtains similar or slightly better results w.r.t. RF and SVM for well represented classes. The second point is related to the comparison between standard approaches trained on the original data and the same methods powered by features learned by our proposal. We can see that the use of the new learned representation improves the performances of both RF and SVM. Again, this fact is particularly evident on critical classes like (1), ( 4), ( 8) (for SVM) and ( 10) (for RF). IV summarizes the results on the REUNION ISLAND time series dataset. Similarly to the previous case, the LSTMbased classifier behaves better than the standard machine learning methods considering Accuracy and F-Measure; still in accord with the previous results, also the classifiers trained on the new features, obtained by the proposed network, outperform their counterparts trained on the original feature space. Conversely to the previous experiment, in this case the highest value of F-Measure is reached by the SVM(LSTM) but we can observe that the LSTM-based classifier still reaches competitive results. Figure 4 reports the per-class F-Measure results on the REUNION ISLAND dataset. Also in this case we can note that the LSTM-based approach works well for low represented and difficult classes (i.e. (8) -"Other Crops") and it remains competitive on all the other classes.

As expected, the combined optimization and learning of a new feature representation along with classification, proper to all deep learning approaches, provides here a better support to discriminate among the different classes. In addition, all these results indicate that the LSTM model is well suited to capture long-short temporal dependencies as opposed to common classification approaches where all the information are managed at the same level forgetting temporal correlations. This is particularly evident on low represented and highly mixed classes: Tree Crops, Summer crops and Truck Farming (resp. Other Crops) for the THAU (resp. REUNION ISLAND) dataset. All these classes are related to agricultural activities whose time patterns are strongly varying due to the heterogeneity of practices, which make the corresponding classes detectable only considering short portions of the time series in which crop conditions are comparable.

We remind that our proposal uses only one LSTM layer while more layers can be stacked together to build more complex architectures [START_REF] Graves | Speech recognition with deep recurrent neural networks[END_REF]. This is out of our scope and we leave this point for future researches, since our main objective is to highlight the quality and the suitableness of RNNs methods to manage and analyze SITS data.

V. CONCLUSION

In this letter we asses the benefit of using Recurrent Neural Network (LSTM) to perform land cover classification via multitemporal spatial data. We have validated the proposed model on two different SITS based datasets showing that the proposed framework efficiently deals with both pixel-based and object-based classification.

The proposed framework proved competitive, yet outperforming compared to classical approaches, with the remarkable advantage of improving the quality of the predictions on "weak" classes from unbalanced datasets. We also highlight that the

Fig. 1 .

 1 Fig. 1. Recurrent Neural Network Unit (on the left) and unfolded structure (on the right).

Fig. 2 .

 2 Fig. 2. The structure of the LSTM unit in which arrows indicate directed connection. Blue lines show the direction in which the information will flow while green lines underline internal flows. Red rectangles represent operations to combine or transform the different information.

Fig. 3 .

 3 Fig. 3. Per Class F-Measure of the different approaches on the THAU dataset.

Fig. 4 .

 4 Fig. 4. Per Class F-Measure of the different approaches on the REUNION ISLAND dataset.

  .

	ID	Land Cover Class	N. of Objects
	(1)	Tree crops	600
	(2)	Forests and woods	2 445
	(3)	Water	556
	(4)	Summer crops	81
	(5)	Winter crops	677
	(6)	Grasslands	3 882
	(7)	Sclerophyll vegetation	2 457
	(8)	Truck farming	227
	(9)	Bare soils	299
	(10)	Salt marshes	236
	(11)	Vineyards	3 735

TABLE I LAND

 I COVER CLASSES AND THEIR CARDINALITY FOR THE THAU TIME SERIES DATASET

  .

	ID	Land Cover Class	N. of Pixels
	(1)	Urban areas	10 000
	(2)	Other built-up surfaces	1 500
	(3)	Forests	10 000
	(4)	Sparse Vegetation	5 095
	(5)	Rocks and bare soil	3 729
	(6)	Grassland	1 744
	(7)	Sugarcane crops	2 832
	(8)	Other crops	1 500
	(9)	Water	1 500

TABLE II REUNION ISLAND

 IIISLAND

TABLE III 5

 III -FOLD CROSS VALIDATION RESULTS ON THE THAU DATASET

	Table

TABLE IV 5

 IV -FOLD CROSS VALIDATION RESULTS ON THE REUNION ISLAND DATASET proposed LSTM-based classification model can be used as feature extractor to learn a new data representation that positively affect the performances of standard classification approaches on SITS data.

The source data are provided by the French Pôle Thématique Surfaces Continentales THEIA (www.theia-land.fr) and preprocessed by the Multi-sensor Atmospheric Correction and Cloud Screening (MACCS) level

2A processor[START_REF] Hagolle | A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of formosat-2, landsat, vens and sentinel-2 images[END_REF] developed at the French National Space Agency (CNES) to provide accurate atmospheric, environmental and geometric corrections as well as precise cloud masks.

ACKNOWLEDGMENT

The authors acknowledge also the National Research Agency in the framework of the program "Investissements d'Avenir" for the GEOSUD project (ANR-10-EQPX-20) for the distribution of the Pléiades satellite images.


Alexandre Dolgui
email: alexandre.dolgui@imt-atlantique.fr

Dmitry Ivanov
email: divanov@hwr-berlin.de

Metaverse supply chain and operations management

Keywords: manufacturing, supply chain management, metaverse, digital twin, blockchain, internet-of-things, Web 3.0, digital supply chain

published or not. The documents may come

Introduction

The metaverse creates a new world where every physical entity (e.g., people, products, and enterprises) has a digital twin. The entities in the metaverse are not just digital replicas of some physical entities; they possess own properties and behaviors influencing behaviors and processes of physical entities. The metaverse is expected to completely change our lives in very near future, much faster as we can think now [START_REF] Kathiala | Look out supply chain-Here comes the Metaverse[END_REF].

What is the metaverse? The term 'metaverse' was coined in 1992 in Neal Stephenson's literary work, Snow Crash [START_REF] Stephenson | Snow Crash[END_REF], visualizing it as a black spherical planet accessible to users through terminals with integrated virtual reality capabilities and where users could appear as avatars (The Economist, 2020). According to Maersk (2022), "The metaverse is the next evolution of the Internet. It's a fusing of the digital and physical worlds powered by technologies, including virtual and augmented reality, blockchain, artificial intelligence, and the Internet of things that connects smart devices". [START_REF] Lovich | What Is The Metaverse And Why Should You Care?[END_REF] defines the metaverse as "a combination of the virtual reality and mixed reality worlds accessed through a browser or headset, which allows people to have real time interactions and experiences across distance." Metaverse develops fast. Digital technology leaders like Nvidia with Omniverse and Facebook with Meta profoundly invest in metaverse solutions [START_REF] Huynh-The | Artificial intelligence for the metaverse: A survey[END_REF]. SupplyOn supplier collaboration platform [START_REF] Holzwarth | Building Viable Digital Business Ecosystems with Collaborative Supply Chain Platform SupplyOn[END_REF] and the Catena-X data ecosystem have been developed in automotive industry allowing for creation of digital product passports and improving sustainability and resilience of supply chains from the ecosystem perspective (Catena, 2022). Siemens and BMW developed smart manufacturing platforms using cloud technology (Siemens 2022, Open Manufacturing 2022). Manufacturing companies like Puma, Nike, Gucci, and Adidas started using the idea of metaverse in marketing and e-commerce to interact with customers allowing them to view or even buy digital versions of their products [START_REF] Barrera | Marketing in the Metaverse: Conceptual understanding, framework, and research agenda[END_REF]. However, the role of the metaverse in supply chain and operations management (SCOM) remains underexplored. [START_REF] Mourtzis | Human centric platforms for personalized value creation in metaverse[END_REF] claim that the metaverse represents a new era in Internet connectivity, characterized by interactivity, simulation, decentralized environment, and persistent reality facilitated by the next evolution of Internet (also known as Web3.0) to combine digital and physical worlds. [START_REF] Lee | Integrated cyber-physical systems and industrial metaverse for remote manufacturing[END_REF] point to conceptual similarities of the metaverse to cyberphysical systems, the application of which to manufacturing has been discussed by [START_REF] Panetto | Challenges for the cyber-physical manufacturing enterprises of the future[END_REF]. A survey by Accenture (2022a) found out that 64% of supply chain management executives believe that the metaverse will make impact in their organizations.

Analysis of the existing literature shows that the metaverse impacts on the SCOM go beyond technological dimension only. The metaverse is not only a technology but a complex sociotechnological phenomenon. In this setting, a scientific approach is needed to reflect on chances, barriers, and challenges that the development of the metaverse will impose on SCOM.

This study aims at advancing our understanding on how the metaverse will influence SCOM. In particular we are interested in exploring the following research questions (RQ):

RQ1: How the metaverse will impact SCOM in the physical world? RQ2: What are potential SCOM processes and decision-making areas in the metaverse? RQ3: What are the mutual impacts from co-existence of physical and metaverse SCOM?

We contribute to literature by proposing a framework describing metaverse SCOM from multiple socio-technological-economical perspectives, i.e., people, organization, technology, management, scope, task, and model. We pose that the further metaverse developments could result in three major decision-making domains in SCOM, i.e., physical supply chain and operations, metaverse supply chain and operations, and coordination of physical and metaverse supply chains and operations. These domains are mutually interconnected.

To provide some guidelines and structure for further research on metaverse SCOM and interrelations between physical and metaverse SCOM, we discuss a future research agenda. We show that future research can explore new opportunities for SCOM stemming from the metaverse, i.e., visibility, computational power for data analytics, digital collaboration, and connectivity. At the same time, we show that new SCOM activities can appear specifically dedicated to the metaverse and leading to novel SCOM processes and decision-making areas (e.g., joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for metaverse and physical worlds, and pricing and contracting for digital products) as well as new performance measures (e.g., virtual customer experience level, availability of digital products, and digital resilience and sustainability).

The remainder of this paper is organized as follows. In Section 2, the results of a SCOPUS search for the metaverse literature related to SCOM are presented. Drawing upon keyword analysis, we propose the metaverse SCOM framework in Section 3. In Section 4, future research questions and new topics focused on metaverse are discussed. We conclude in Section 5 by summarizing major insights of this study and pointing to some future extensions.

Analysis of main topics in research on the metaverse and SCOM

To understand the state-of-the art in research on the metaverse and SCOM, we first run a SCO-PUS search organised as follows: TITLE-ABS-KEY ( metaverse ) AND ( LIMIT-TO ( DOCTYPE , "ar" ) ) AND ( LIMIT-TO ( SUBJAREA , "ENGI" ) OR LIMIT-TO ( SUBJAREA , "BUSI" ) OR LIMIT-TO ( SUBJAREA , "DECI" ) )

We searched for "metaverse" in the titles, abstracts, and keywords of the journal articles in the business and management, decision sciences, and engineering areas. The search yielded 217 papers and 119 keywords with a minimum threshold of using a particular keyword in at least 3 articles. The result of the VOS Viewer co-occurrence analysis is presented in Fig. 1.

Fig. 1. Metaverse and SCOM research map

We carefully analysed the keywords identified by SCOPUS and structured them based on three frameworks. First, we used a framework from organisation theory, namely Leavitt's diamond model, which is based on four elements: structure, task, people and technology. In addition, we consider the 3D framework of Industry 4.0 (Ivanov et al., 2021a), which classifies technology, management, and organisation elements, and the digital twin framework proposed by Freese and Ludwig (2022), which suggests scope, actor, asset, flow reference object, performance measurement, and supply chain process as major SCOM digital twin elements. Integrating these frameworks, we propose the following seven elements to be included in the metaverse SCOM framework: technology, people, management, organisation, scope, task, and modelling (Table 1). It can be observed in Table 1 that the existing research on the metaverse and SCOM covers a broad socio-technological-economical spectrum. On the one hand, our analysis allows to identify key digital technologies enabling the metaverse. On the other hand, the key role of people and human-machine interface becomes evident through the keywords represented in Table 1.

The metaverse enables new organizational forms and management capabilities (e.g., visibility and interaction). A large variety of artificial intelligence-based modeling methods supports decision-making tasks in forecasting, manufacturing, and contracting in different system scopes.

Metaverse SCOM framework

Based on the keyword analysis, we can now propose a metaverse SCOM framework (Fig. 2).

Fig. 2. The metaverse SCOM framework

In Fig. 2, we combine keywords identified by the SCOPUS search with our integrative analysis of the relevant frameworks such as digital twins [START_REF] Negri | A review of the roles of digital twin in CPS-based production systems[END_REF][START_REF] Badakhshan | Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions[END_REF][START_REF] Berti | Digital Twin and Human Factors in Manufacturing and Logistics Systems: State of the Art and Future Research Directions[END_REF][START_REF] Burgos | Food Retail Supply Chain Resilience and the COVID-19 Pandemic: A Digital Twin-Based Impact Analysis and Improvement Directions[END_REF][START_REF] Frazzon | Intelligent Methods and Systems for Decision-Making Support: Toward Digital Supply Chain Twins[END_REF][START_REF] Huang | Building blocks for digital twin of reconfigurable machine tools from design perspective[END_REF], cloud and digital supply chain (Ivanov et al., 2022;[START_REF] Maccarthy | The Digital Supply Chain-emergence, concepts, definitions, and technologies[END_REF][START_REF] Zhang | The cloud, platforms, and digital twins-Enablers of the digital supply chain[END_REF] Further, in Fig. 3 we illustrate the extension of traditional SCOM understanding as a "a crossdepartment and cross-enterprise integration and coordination of material, information, and financial flows to transform and use the supply chain resources in the most rational way along the entire value chain, from raw material suppliers to customers" (Ivanov et al., 2021b, p. 9) toward a triple-SCOM view whereas physical, metaverse, and physical-metaverse SCOMs coexist.

Fig. 3. SCOM in the metaverse era Fig. 3 depicts that physical and metaverse worlds are connected through digital technology such as augmented/virtual reality, Blockchain, Artificial Intelligence, Internet-of-Things, 5G/Edge computing, ERP, Big data analytics, and simulation [START_REF] Brintrup | Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing[END_REF][START_REF] Cai | Platform supported supply chain operations in the blockchain era: supply contracting and moral hazards[END_REF][START_REF] Chabanet | Toward digital twins for sawmill production planning and control: benefits, opportunities, and challenges[END_REF][START_REF] Cui | AI and Procurement[END_REF][START_REF] Dolgui | 5G in Digital Supply Chain and Operations Management: Fostering Flexibility, End-to-End Connectivity and Real-Time Visibility through Internet-of-Everything[END_REF][START_REF] Dubey | Empirical Investigation of Data Analytics Capability and Organizational Flexibility as Complements to Supply Chain Resilience[END_REF][START_REF] Elmachtoub | Smart "Predict, then Optimize[END_REF]. Smart devices and sensors from physical products along with 3D printers represent another part of data sources for the metaverse. In the metaverse, digital customers (i.e., avatars) act in the digital markets where digital products are offered and sold using digital money (probably, a mix of physical and digital products can be considered, too). Managers use digital collaboration spaces for sourcing, production, and logistics coordination. Digital stores, factories, warehouses represent the supply chain in the metaverse, which can be digital replicas of physical stores, factories, and warehouses, or represent new, additional entities, which do not exist in the physical supply chain.

Open research questions

In this section, we outline open research questions related to the metaverse SCOM.

Area 1: Scope and task

The scope of the metaverse SCOM will cover digital products, digital factories and warehouses, digital supply chain, and digital ecosystems. The metaverse supply chain is not just a digital replica of a physical supply chain. The digital and physical supply chains evolving autonomously but co-jointly. When we assume that a digital twin is a digital replica of a physical supply chain, then the metaverse is more than a digital twin. On the one hand, the metaverse enhances decision-making support and analytics applications for physical SCOM. On the other hand, the digital and physical supply chains mutually influence each other [START_REF] Liu | Web-based digital twin modeling and remote control of cyber-physical production systems[END_REF][START_REF] Lv | Digital Twins on the Resilience of Supply Chain Under COVID-19 Pandemic[END_REF].

For example, an increased popularity of a product in the metaverse can lead to an increased demand for this product in the physical supply chain. A timely recognition of these trends through data analytics can help supply chain managers to prepare for the peak load. The metaverse data analysis can also be used for new product introductions in the market and decisions on initial order quantityfor example, a product can be first introduced in the metaverse and the sales/inventory data from the digital supply chain can be used to setup the physical supply chain processes. In another example, a product shortage in the physical supply chain can be substituted by an increased supply of this product in the metaverse so that customers (or their avatars) who cannot buy the physical product could obtain it in the digital form. This is a novel context for supply chain resilience management.

Assuming that people will have more and more activities in the metaverse, we can expect new SCOM activities specifically dedicated to the metaverse and leading to appearance of novel SCOM processes and decision-making areas. For example, joint demand forecasting for metaverse and physical products belongs to a new research area. Since digital products will also require some storage place in the metaverse, digital inventory allocation in the metaverse can arise as a novel optimization context. Pricing and contracting for digital products as well as new performance measures (e.g., virtual customer experience level and availability of digital products, and digital resilience and sustainability) can motivate new research. Through digital analytics, testing and forecasting customer and suppliers behaviors can be used for demand, inventory and capacity planning.

Inventory management research can also be innovated through the metaverse. For example, a part of customers might like to have both physical and digital products, another partonly physical, and another oneonly digital products. E.g., a luxury car, which can be too expensive in real life can be purchased in the metaverse. Competition between digital and physical products can lead to new interesting problem settings in pricing and inventory management. In some cases, digital products can even be wanted more as physical ones-a new setting for revenue management. Sourcing and production planning in the metaverse SCOM can be adjusted by digital collaboration places with an improved delivery visibility and coordination. In addition, physical products might be increasingly supplemented by some digital services, and digital products can have some physical add-ons. In this setting, sourcing and production planning can encounter novel and counter-intuitive decision-making problems.

Area 2: Management

As indicated in Fig. 2, three SCOMs could exist when the metaverse becomes an important part of everyday life -SCOM for physical world, SCOM for digital world, and SCOM for coordinating physical and digital worlds. The metaverse can be used for decision-making support in physical SCOM through enhanced management capabilities such as visibility, computational power for data analytics, digital collaboration, and connectivity [START_REF] Dolgui | 5G in Digital Supply Chain and Operations Management: Fostering Flexibility, End-to-End Connectivity and Real-Time Visibility through Internet-of-Everything[END_REF].

Through supply chain mapping, it becomes possible to obtain more accurate, real-time data on lead-times, delays, deliveries, shortages, physical locations of containers and trucks, and costs (MacCarthy et al., 2022). Forecasted, known-unknown becomes knowable. For example, in a metaverse "collaboration room", supply chain managers could "review expected sales forecasts, projected production plans and possible supplier limitations that could affect manufacturing volume. They could also see, on an immersive supply chain network map, exactly where inventory is, virtually walk through key ports to identify possible shipping delays because of congestion, and model possible alternatives to keep products moving to the right stores and shelves." (Accenture, 2022b).

Resilience management can be enhanced by disruption recognition, impact prediction, recovery actions [START_REF] Ralston | Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss[END_REF]Ivanov, 2021;Ivanov and Dolgui, 2021;[START_REF] Ivanov | Blackout and Supply Chains: Performance, Resilience and Viability Impact Analysis[END_REF]. Using digital twin-based simulation environments, managers can analyse different scenarios in the virtual world using the digital supply chain before implementing decisions in the physical supply chain. Recognizing bottlenecks and enforcing supply chains for peak loads (e.g., demand increase) and supply disruptions will be easier and stress-testing of supply chains can be performed with a higher knowledge awareness [START_REF] Aldrighetti | Costs of resilience and disruptions in supply chain network design models: a review and future research directions[END_REF][START_REF] Aldrighetti | Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments[END_REF]Ivanov and Dolgui, 2022a).

Sustainability management can be improved through transparency about carbon emissions, visibility about the entire product life cycle of products and associated environmental footprints.

The digital supply chain can help in tracing the upstream suppliers to ensure that suppliers do not use child work and produce products according to sustainability standards and laws.

Area 3: Technology

Huynh-The et al. ( 2023) point to six major technological elements for the metaverse, i.e., a digital twin (cyber-physical interface), neural interface (brain-computer interface), machine vision (virtual/augmented reality), networking (e.g., multi-access edge computing), blockchain (data collection, storage, sharing, and management), and natural language processing (e.g., textto-speech processing). [START_REF] Bhandal | The application of digital twin technology in operations and supply chain management: a bibliometric review[END_REF] point to Internet-of-Things, Blockchain, Artificial Intelligence and Data analytics, augmented and virtual reality, and Industry 4.0 as digital twin enablers. In addition, through 3D printing, production can be triggered by customers. Customers can also design products and they will be produced on demand [START_REF] Boute | Dual sourcing and smoothing under nonstationary demand time series: Reshoring with speedfactories[END_REF][START_REF] Peron | Additive or Conventional Manufacturing for Spare Parts: Effect of Failure Rate Uncertainty on the Sourcing Option Decision[END_REF]. This will have implications on supply chain complexity and environmental footprint along with increased customer satisfaction. Digital platforms and supplier collaboration portals are used to ensure collaboration and communication in Industry 5.0 [START_REF] Reim | Enabling collaboration on digital platforms: a study of digital twins[END_REF][START_REF] Holzwarth | Building Viable Digital Business Ecosystems with Collaborative Supply Chain Platform SupplyOn[END_REF][START_REF] Sawik | A linear model for optimal cybersecurity investment in Industry 4.0 supply chains[END_REF]. End-to-end visibility, which is so important for both proactive and reactive decision making, is supported across the supply chain by ERP systems, Blockchain, and T&T systems [START_REF] Roeck | Distributed ledger technology in supply chains: a transaction cost perspective[END_REF]Choi et al., 2022;[START_REF] Li | Blockchain-supported business model design, supply chain resilience, and firm performance[END_REF][START_REF] Maccarthy | The Digital Supply Chain-emergence, concepts, definitions, and technologies[END_REF].

Digital twins can be enabled by technologies of different scopes [START_REF] Boyes | Digital twins: An analysis framework and open issues[END_REF][START_REF] Nguyen | Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review[END_REF]. CAD/CAM systems are applied at the product level while MES and ERP systems enable building of digital twins of processes and organisations. At the supply chain level, special software such as anyLogistix in combination with external data sources (e.g., data from logistics service providers, weather data, financial market data) are used to build supply chain digital twins (Ivanov and Dolgui, 2021;[START_REF] Burgos | Food Retail Supply Chain Resilience and the COVID-19 Pandemic: A Digital Twin-Based Impact Analysis and Improvement Directions[END_REF]. Future research areas highlight both a technical understanding of system integration and interoperability and management conceptualisation of needs and limits for data-driven decision-making support. Technologies allow for the integration of models with external data sources and ensure interactions with other digital twins.

Area 4: People

The metaverse will change the work and role of people in SCOM. Automatic responses with minimal human intervention, new standards for working places and remote work, collaboration of people (virtual meeting platforms), and human-robot collaboration are just some examples (Rozanek et al., 2022;[START_REF] Sheu | Can We Work More Safely and Healthily with Robot Partners? A Human-Friendly Robot-Human Coordinated Order Fulfillment Scheme[END_REF][START_REF] Saghafian | The internet of things and information fusion: who talks to who?[END_REF][START_REF] Sun | Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations[END_REF]. The metaverse is being developed and used by people, and at the same time it changes the human behaviours and SCOM decision-making.

Decisions in SCOM depend on the expertise of the manager, knowledge and skills they exhibit, and their access to real-time data and information [START_REF] Sgarbossa | Human factors in production and logistics systems of the future[END_REF]. The metaverse can help managers providing decision-making support by acquiring real-time data and simulating potential outcomes of certain decisions (e.g., alternative recovery policies after a disruption or changes in an environmental footprint due to a supply chain redesign). Digital twins can also consider the competence of making decisions (e.g., placing orders in an inventory control system). Most centrally, the metaverse offers real-time, data-driven decision-making support.

Further research is needed to examine the impacts of a continuous access to real-time data on managerial decision-making. In addition, behavioural aspects of data-driven decision-making and cognitive biases in human-artificial intelligence interactions belong to the novel topics when digital twins can be explored in SCOM research [START_REF] Fahimnia | Behavioral operations and supply chain management-a review and literature mapping[END_REF][START_REF] Fu | Un"fair machine learning algorithms[END_REF][START_REF] Sun | Predicting human discretion to adjust algorithmic prescription: A large-scale field experiment in warehouse operations[END_REF]. At the manufacturing system level, human-robot collaboration is one of the central digital twin-related future research topics [START_REF] Sheu | Can We Work More Safely and Healthily with Robot Partners? A Human-Friendly Robot-Human Coordinated Order Fulfillment Scheme[END_REF]. [START_REF] Mourtzis | Human centric platforms for personalized value creation in metaverse[END_REF] stress the human-centric perspective of the metaverse echoing an integration of human-centricity, resilience, and sustainability in Industry 5.0 framework (Ivanov, 2022a).

Area 5 Organisation

Technology determines organisation. The metaverse will not only mirror physical SCOM organisations but also create and develop new business and operational models. Through digital twins, novel organisational constructs such as digital manufacturing, cloud supply chain, and collaborative platforms arise [START_REF] Sharma | Digital Twins: State of the art theory and practice, challenges, and open research questions[END_REF]Ivanov et al., 2022). Examination of the metaverse-driven transformations in the organisation of SCOM can be conducted in future research areas where impactful and substantial contributions can be made. In addition, digital twins can lead to new organisational structures and a redistribution of decision-making competencies across departments. Metaverse solutions can be applied to factory design and planning through simulation of their digital twins. In the created virtual simulation environments, processes and flows can be represented, analyzed, and improved. New organizational forms (e.g., cloud supply chains, intertwined supply networks, ecosystems) and new categories in SCOM such as creator economy, discovery, and digital experience can appear [START_REF] Ivanov | Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. A Position Paper Motivated by COVID-19 Outbreak[END_REF].

In the context of viability, digital technology allows for the implementation of the viable supply chain model [START_REF] Ruel | Supply Chain Viability: Conceptualization, Measurement, and Nomological Validation[END_REF][START_REF] Ivanov | Viable Supply Chain Model: Integrating agility, resilience and sustainability perspectiveslessons from and thinking beyond the COVID-19 pandemic[END_REF][START_REF] Ivanov | Post-pandemic adaptation and development of supply chain viability theory[END_REF]. Visibility, reconfigurable manufacturing systems, and additive manufacturing, along with analytics and digital collaboration tools, are vital for viable manufacturing and supply chains. In light of the increasing resource shortages in physical supply chains due to component (e.g. semiconductors) shortage, workforce variability, energy blackouts, and inflation (Ivanov and Dolgui, 2022b;[START_REF] Hägele | Supply chain resilience: a tertiary study[END_REF], the importance of viable supply chains and the metaverse will continue to grow in the future.

Following [START_REF] Ivanov | Viable Supply Chain Model: Integrating agility, resilience and sustainability perspectiveslessons from and thinking beyond the COVID-19 pandemic[END_REF], "the Viable Supply Chain Model is based on adaptable structural network designs for situational supply-demand allocations and, most importantly, the establishment and control of adaptive mechanisms for transitions between the structural designs (Ivanov, 2021e). Moreover, supply chain viability and the ecosystem view have been synthesized in the lens of the human-centred ecosystem perspective by Ivanov and Dolgui (2022a)." In addition, the reconfigurable supply chain framework can be considered a part of future Industry 5.0 developments [START_REF] Dolgui | Reconfigurable supply chain: The X-Network[END_REF]Ivanov, 2022a). [START_REF] Dolgui | Reconfigurable supply chain: The X-Network[END_REF] note that "supplementing the reconfigurable manufacturing concept [START_REF] Zennaro | Big size highly customised product manufacturing systems: a literature review and future research agenda[END_REF][START_REF] Battaïa | Sustainable and energy efficient reconfigurable manufacturing systems[END_REF], the reconfigurable supply chain adds three specific features: active behaviour of network elements, networking effects across multiple structures and their dynamics (i.e., organizational, information, financial, technological, energy), and network complexity (i.e., multi-echelon supply chains).

The reconfigurable supply chains are characterized by structural and process variety, which is beneficial for supply chain resilience."

Area 6 Modelling

Analytics capabilities offered through the metaverse can hardly be imagined now to full extent. Modelling in the metaverse will be based on shifting historical data-based forecasting for supply chain and operations planning methods toward real time, data-driven decisions (Fig. 4). Optimisation, discrete-event simulation, neural networks, machine and reinforcement learning, agent-based modelling and system dynamics allow for the implementation of the full variety of descriptive, predictive and prescriptive algorithms in SCOM [START_REF] Cavalcante | A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing[END_REF][START_REF] Rai | Machine learning in manufacturing and Industry 4.0 applications[END_REF][START_REF] Fu | Un"fair machine learning algorithms[END_REF][START_REF] Kusiak | Predictive models in digital manufacturing: research, applications, and future outlook[END_REF][START_REF] Rolf | A review on reinforcement learning algorithms and applications in supply chain management[END_REF]. While real-time data-driven models constitute a narrow view of digital twins (i.e., a digital twin as a stand-alone software package), in a broader sense, digital twins can be considered as a combination of different information systems and models. Seamless digital and physical integration can become the centric element of the SCOM metaverse. For example, imagine a product that knows its location, inventory status, price, and costs. Using edge computing, an algorithm would trigger automatic replenishment, routing, pricing, and demand prediction decisions enhancing margins, product availability, on-time delivery, and overall profitability. New computational capacities for supply chain and operations analytics and the use of synthetic data along with industrial Internet-of-Things can be used to predict customers and supplier behaviors in terms of demand recognition and delivery accuracy. Future research can shed more light on the transition from offline to real-time data-driven modelling, revealing its value and barriers through improved end-to-end visibility in the supply chain.

Conclusion

The metaverse and Web 3.0 represent new and strong trigger for further evolution of SCOM.

They not only create a new, digital world with specific properties and behaviors replicating behaviors and processes of physical entities, but also influence physical SCOM. Despite some fragmented literature with the focus on the metaverse and SCOM, there is a lack of understanding of how the metaverse will impact SCOM in the physical world, what are potential SCOM processes and decision-making areas in the metaverse, and what are the mutual impacts from co-existence of physical and metaverse SCOM.

Driven by these questions, our study aimed at advancing our understanding on how the metaverse will impact SCOM drawing upon cyber-physical system, digital twin, cloud and digital supply chain, and Industry 4.0/Industry 5.0 concepts. We proposed a framework for metaverse SCOM encompassing seven socio-technological dimensions, i.e., organization, management, people, technology, scope, task, and modelling.

Our analysis shows that in future we can expect a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of physical and metaverse worlds. We offered a structured future research agenda pointing to new research questions and topics stemming from metaversedriven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can appear specifically dedicated to the metaverse and novel SCOM processes and decision-making areas (e.g., joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for metaverse and physical worlds, and pricing and contracting for digital products) as well as new performance measures (e.g., virtual customer experience level, availability of digital products, and digital resilience and sustainability).

  , collaborative networks[START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF], reconfigurable supply chain[START_REF] Dolgui | Reconfigurable supply chain: The X-Network[END_REF], cloud manufacturing[START_REF] Moghaddam | Collaborative Service-Component Integration in Cloud Manufacturing[END_REF], open manufacturing[START_REF] Kusiak | Open manufacturing: A design-for-resilience approach[END_REF], Physical Internet[START_REF] Pan | Physical internet and interconnected logistics services: research and applications[END_REF], and Industry 4.0/Industry 5.0[START_REF] Yin | The evolution of production systems from Industry 2.0 through Industry 4.0[END_REF][START_REF] Tang | The strategic role of logistics in the Industry 4.0 era[END_REF][START_REF] Zennaro | Big size highly customised product manufacturing systems: a literature review and future research agenda[END_REF][START_REF] Winkelhaus | Logistics 4.0: a systematic review towards a new logistics system[END_REF] Choi et al., 2022; Ivanov, 2022a).

Fig. 4 .

 4 Fig. 4. Modelling in the SCOM metaverse framework

Table 1 .

 1 Metaverse SCOM elements

	Technology	Organi-	People	Management Scope	Task	Model-
		sation					ing
	Virtual Reality	Industrial	Virtual	Decision-	Web 3.0	Market-	Big Data
	Augmented Reality Blockchain Artificial Intelli-	Metaverse Real-Time Sys-tems	Worlds E-learning Extended Reality	making Visibility Sustainability	Digital Assets Digital Devices	ing User ex-perience Forecast-	Deep Learning Machine Learning
	gence	Smart	Second	Performance	Multi-	ing	Neural
	Mixed Reality	City	Life	Multi Criteria	media	Smart	Networks
	3D Computer Graphics		Avatar	Decision-making	Con-tents	contract	Mathe-matical


Florence Lévy

Marylène Lieber

Special issue: 'Migrants and clandestinity' Numéro spécial: 'Migrants et clandestinité'

Keywords:

Some Northern Chinese migrant women end up prostituting themselves after their arrival in Paris. If one must relate this phenomenon to their illegal status and their lack of information about the host society, this article shows nevertheless that Chinese prostitutes in Paris have developed know-how and tactics to survive in an extremely precarious situation. Focusing on those coping strategies allows us to understand that, despite a form of resignation, prostitution is seen as the best way to bridge the gap between the reality these women face in France and the dreams they had before departure.

tered the surprising phenomenon of middle-aged, plainly dressed Chinese women street-walking as a means to earn a livelihood. Furthermore the ploys these women use to attract costumers differ from the usual techniques of traditional prostitutes. They typically try to blend in with the general population and do not draw attention to themselves. Generally they work independently and do not become part of a gang or utilize a pimp. Thus, the activities of this group of women are characterized by the fact that, as illegal immigrants, they find themselves in a double bind: they must try to be invisible and thereby avoid drawing the attention of the authorities, and yet still be visible enough to attract customers. In dealing with this Catch-22 situation, these migrants have invented original strategies.

The present article describes this recent phenomenon without entering into the ethical and normative debates on prostitution that posit a stark alternative between 'criminalization' and 'victimization'. Rather, we highlight the decision-making processes that shape the migratory trajectories of these women and the reasons they have for turning to prostitution. Therefore we begin by examining what Chinese prostitutes in Paris think about prostitution and, in broader terms, their overall migratory goals. The fact of their illegal status is central to understanding their choices, as it entails several layers of constraint and restricts the realm of possibilities for them. It also sheds light on the question of how these women, who say they never thought they would take up such an activity, decide to turn to prostitution. Lastly, we present some of the coping strategies they have invented, and underline how, in a very unstable environment, these migrants have developed tactics to make the best of the worst. 1

Migratory projects that failed

Most Chinese prostitutes in Paris belong to a larger migratory category known as 'dongbei', a term that refers to three provinces previously known as Manchuria. A description of this broader flow allows us to understand the specificities of these women's migratory trajectories, as they are somewhat atypical in comparison to traditional Chinese migration. First, migration from dongbei is quite new, and, in contrast to the old Chinese diaspora, does not rely on networks or traditional migratory channels. On the contrary, it resembles pioneer migration by individuals who have neither migratory know-how nor help and support upon arrival. Second, the majority of these migrants are middle-aged women. Third and last, these women were educated urban residents and belonged, before their departure, to the 'middle' classes. 2

P R O O F O N L Y

Lévy & Lieber Northern Chinese women in Paris 631

For the most part, these new dongbei immigrants used to work for Stateowned enterprises that were once considered the center of the communist economy (see Kernen & Rocca, 1999). They were part of a privileged class, and some of our interviewees describe their life in China as quite comfortable. Since the reforms and the failure of these enterprises in the 1990s, their status has been declining. Due to economic instability, most of them lost their work and had to rely on temporary jobs. Others lived in fear of losing their jobs with the resulting worries for their financial futures. This phenomenon was reinforced by the fact that many of those who decided to migrate were divorced women who suffered from the stigmatization of divorce that disproportionally affects women. They had seen their standard of living fall dramatically, and many explained that they could no longer maintain a decent quality of life for their families. Thus, the reasons for leaving China were mainly bound to economics and to the rising costs of what they consider their basic needs. 3 During our interviews, a number of salient common points emerged. By immigrating abroad, Northern Chinese migrants expected, first of all, to be able to earn an income that would allow them to provide a good education for their child(ren) as university fees in China today frequently cost more than one year's worth of the local average revenue. Others wanted to save enough money to pay for their aging parents' hospitalization in a situation where health care has been privatized and has become extremely expensive. Mrs Xu, an interviewee, explained that she wants to save 'capital for her old age' as she was not certain that her government pension would be available. Similarly, some people feared not finding a good job, and left hoping to make money in order to open their own business. Other women mentioned their desire to save money to buy an apartment before theirs was torn down due to large urbanization projects. Only a few women mentioned that they wanted to 'see the world'. Overall, they anticipated finding better employment opportunities. Some of them also hoped for sentimental encounters, with the idea of starting a new family in France. In this respect, most of the migrants had short-or middle-term projects only, expecting to return to China in three to five years' time.

Our interviewees thought they could fulfill their wishes by spending a few years abroad. However, upon arrival in France, they discovered a huge gap between their goals and their living conditions in Paris. Misinformed before departure, these women described their despair when they realized the horrible conditions of the Chinese dormitories and the instability of their compatriots' existence. They lived without any privacy in cramped and often insalubrious dormitories housing dozens of men and women. Because they did not have a job, many did not go out for fear of being arrested by the police. Nevertheless, the dormitory setting did offer the new immigrants their first opportunity to get hold of information. In their terms, it was here that they realized their life in France would not be easy, and that they would have to modify or postpone the realization of their initial goals.

Typically the story of their entry into prostitution runs as follows. Their lack of knowledge of French and their illegal status soon forces the newcomers from Northern China to enter the Chinese 'community' work market. This market is dominated by Southern Chinese from the region of Wenzhou and has no room for solidarity. On the contrary, it is characterized by divisive intra-group tensions and structures of exploitation (see Lieber, 2008). The established Southern Chinese business owners tend to look down on people from Northern China. As a result, recently arrived immigrants, both men and women, have difficulty finding jobs in this doubly ethnicized work market and they end up occupying subordinate positions. For example, in the catering business, men from Northern China seldom find a job as first cook and are most often relegated to the rank of kitchen helper. Further discrimination is also seen in the clothing industry, where Northerners are chosen as warehouse personnel rather than textile workers. For Northern Chinese women, this kind of segregation restricts the range of possible work opportunities in even more drastic ways. For example, most of them cannot find work in the garment industry because they are said to be bad seamstresses. Their only remaining avenue is to work as live-in maids for the established Southern Chinese community.

To comprehend the reasons why some of these women choose prostitution over domestic service, it is essential to understand the dire working conditions of this type of employment. Most of the women we interviewed were horrified at the way they had been or were being treated. In addition to the very low salary -between 500 and 900 euros a month -they complained about the workload. A typical day would include tasks ranging from childcare to extended housework in both the household and the family business. Needless to say, official regulations were not applied to these very long working hours. Furthermore, our interviewees identified the disrespectful attitude of their employers as the most degrading aspect of this work. The women we interviewed felt they were often deliberately humiliated by such tactics as very low food rations, insults or even threats or physical violence. The total lack of privacy and freedom drives many women to quit such jobs.

A second aspect of their choice to enter prostitution resides in the ethnicized nature of the French labor market. Apart from work in the catering and restaurant, garment or domestic-service sectors, no other jobs are open to non-French-speaking, middle-aged Chinese women in Paris. Some go through repeated sequences of employment as domestic servants and

P R O O F O N L Y Lévy & Lieber
Northern Chinese women in Paris 633 unemployment, often with the conviction that temporary jobs are still more desirable than working as a maid in Wenzhou families. Finding a job with a French family is hardly an option, for reasons of language and lack of networks. Some women make the best of their situation and work as 'entrepreneurs' -as scavengers, as masseuses and vendors of curios. Finally, a certain number of them take up prostitution.

The resignation of a marginalized population

Prostitution is not an ordinary occupation, and the attitudes of the women we interviewed were ambivalent. On the one hand, even after they start 'to do it' as they say, they continue to condemn this kind of activity. On the other hand, they present it as a direct consequence of their migration and lack of social networks -prostitution appears to be an answer to the extreme instability of their existence due to their illegal status. Mrs Cheng, an interviewee, describes the situation as when 'men do not act as men anymore and women do not act as women anymore', underlining the extreme difficulties they have to face. The main problem for these women is to survive in a foreign environment where they lack control over their life conditions and choices. Most of them explain that they 'were forced' and 'had no other choice'. Mrs Liao sums up this feeling by saying: 'it is the only option, when you have no choice'. Acknowledging economic and social constraints, they adapt.

Taking the first step

Entering into prostitution is a gradual process. The first step shows the Northern Chinese women's willpower softening. Confronted with professional and economical difficulties, they progressively change their minds after having publicly refused such an option. Typically, 'friends' see that they lack employment and money and suggest they 'do it'. For example, Mrs Yu's sister let her experience what it is like to work as a maid before introducing the idea of prostitution, allowing her 'slowly to understand' that it was the best option. As Mrs Yu was quite reluctant, her sister found her a client and arranged for the hotel. Mrs Liao had a similar experience; she learned through the 'buddy system', waiting in front of the building or in the kitchen while her friend entertained clients. Thus, all the women we interviewed mentioned someone who acted as a go-between for their 'first time'.

This kind of introduction into prostitution is presented as a free service between friends. However, Mrs Xie paid 30% of the price of her first tricks to the friend who acted as a go-between. She said, however, that in exchange for this percentage her friend provided resources to help her overcome problems related to her inexperience, to her shame at 'doing it' and to the dangers of soliciting in the street. This informal collaboration lasted for a few weeks until Mrs Xie became self-employed. She considers it not to have been a real cost but a way to increase her experience, skills and confidence. This kind of patronage helps beginners enter the business and learn the trade. In addition, Mrs Xie pointed out that the clients' constant demand for 'new' women provides another incentive for women to take up prostitution.

Interestingly, our observations as well as the testimony of many women we interviewed, suggest that this patronage is not necessary. Regardless of their status, age or nationality, they seem to be the target of street-level sexual harassment in areas such as Belleville, Barbès or Aubervilliers. For example, Mrs Wang explains that she was frequently approached by strangers before she decided to work as a prostitute, and many others had comparable experiences. They seem to be considered particularly easy and defenseless by men from these areas, some of whom told us that these Chinese women would sleep with anyone in exchange for a crate of vegetables or an invitation to dinner. 4 The perpetrator often does not fear reprimand, as such harassment goes unreported due to the language barrier and the illegal status of this population. Consequently, most of these women have no choice but to turn their head and go on their way. In this respect, there is a continuum of street-level harassment of Chinese women and soliciting of Chinese prostitutes. Because of this blurring of boundaries, Chinese prostitutes say they do not have to solicit actively, as their mere presence in the street seems to generate sexual offers from strangers. We could say these women have simply changed the way they respond to these sexual offers: they now accept the advances they used to refuse. Long before they actually 'did it', Northern Chinese women in these neighborhoods were regarded as 'whores'. By the same token, entering into prostitution does not change anything in terms of reputation, as the stigma has pre-dated their decision to 'do it'. Surprisingly, then, there is no big plunge, just a little switch -and a slow shift into the world of prostitution. By working as prostitutes, these women in a certain sense have simply appropriated the sexualized image others already have of them.

Blurring boundaries

Our interviewees enjoyed talking about numerous subjects but not about their activities as prostitutes. This had several explanations. First of all, these women

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were caught in a double bind -between their illegal status and their work. On the one hand, they could be arrested and deported at any time under residency and work-permit regulations. They believed this to be the worst thing that could happen to them, and were compelled to live with the uncertainty this situation produced. On the other hand, French legislation considers prostitution and the action of soliciting a criminal offense. These women thus lived in constant fear of encounters with the police. Furthermore, sex is always a difficult topic to talk about and the subject is complicated by the stigma of prostitution.

Most of the women we interviewed were ashamed to work as prostitutes and tried to guarantee themselves as much anonymity as possible. They did not speak about the subject directly and adopted a strategy of distancing. They blurred the reality of prostitution and aimed to present themselves as respectable women. This can be read as a way of practising prostitution without losing one's self-esteem. Indeed, most of the prostitutes we interviewed insisted on the unprofessional and temporary aspect of their activity. For example, as Mrs Xie presented it: 'I went out as a fill-in, as a substitute, not as a professional.' These attempts at blurring also apply to clients. Some women view prostitution as a way to meet French men and, ideally, to marry, thus gaining the right to stay in France legally and attaining their migratory goals. In this sense, presenting themselves as amateur prostitutes can be a strategy. As most of the Northern Chinese women who have succeeded in obtaining residency permits have obtained their status through marriage, the women consider it important to keep economic sexual transactions open to potential romantic developments. For example Mrs Xie, who wishes she could marry, flirts with her clients and explains to them that she 'does it' only as an exception, to be able to survive.

Clients participate actively in this blurring of categories. Some of them act as lovers or suitors; they maintain a kind of semi-friendship, regularly inviting these women to have a cup of coffee or dinner, or sending frequent messages or calling. Some even invite them to live with them and, more seldom, marry one of them. Women talk about these men as 'old friends'. One interviewee even visited an old client in the hospital. This kind of relationship would hardly be conceivable for most French prostitutes, who, on the contrary, insist on drawing a clear boundary between professional and private life (see Gil, 2005). Even after marriage, the blurring of categories continues, as both partners pretend not to be aware that the wife still works as a prostitute. To justify leaving the apartment, Mrs Cheng tells her husband that she is going out to clean someone's house. She knows he is aware of her real activity, but pretends not to know, as he benefits from her financial support. This situation, which the law would consider pimping, allows Mrs Cheng to keep her partner and thus her residency permit.

Chinese prostitutes do not openly disclose their activity. They 'do it' only because of the instability of their situation in France and the hope that they will find a way out which will allow them to go home without having lost their respectability. Thus they develop tactics for winning such respectability by creating strategies for coping with reality. The blurring of boundaries is related to both maintaining self-esteem and minimizing the risks they face. As illegal migrants and prostitutes, these women must adopt a survival strategy linked to the assignment of invisibility and marginalization.

Coping strategies of illegal immigrants

Prostitutes are exposed to violence as they work in the street and regularly meet strangers. 5 In our interviews, they reported the need to avoid several types of dangers. The obvious threat they face is violence from clients and from being on the street. However, their major preoccupation revolved not around their clients but around their fears of encounters with the police and the associated fear of deportation. This fear was aggravated by the need to be visible in order to attract clients. Indeed, crackdowns on illegal immigration often take the form of repression of prostitution, and most of the Chinese women arrested for solicitation are eventually prosecuted for illegal residency and deported. Thus, these women are targets for the police both as illegal migrants and as prostitutes. In what follows, we will present the various tactics they develop to diminish the dangers they face.

Invisibility tactics

Given the intensification of policing, Chinese prostitutes develop tactics to make their identification difficult. They adapt their dress and their behavior to fit into the crowd -they do not dress provocatively or apply ostentatious make-up. In this respect, these women break down the usual stereotypes of professional prostitutes and, to the contrary, do their best to appear nonprofessional. They walk continuously and adopt a purposeless attitude, in order to appear to be strolling city-dwellers to anyone except their clients. Walking with a shopping basket is, for example, an explicit ploy. In another example, they sit at café terraces acting like normal consumers, or in the large illuminated windows of lunch counters, where they can see and be seen.

Their soliciting is unnoticeable and often passive. Mrs Cheng told us that all she needed was a glance from a client in order to understand that she was to meet him later in the subway and follow him. Mrs Wang explained that

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Northern Chinese women in Paris 637 the glance is a key element, as she needed at the same time to measure the risk of undercover policemen. Of course, policemen have their countertactics, one of which is to pretend they are clients in order to identify prostitutes. To avoid this kind of situation, our interviewees said that they regularly pretend not to understand what the client wants until they are certain he is not a policeman. Mrs Cheng uses her intuition based on the encounter to make sure that the client is really a client. She settles at a bus stop next to a red light, pretending she is waiting for the bus to come. If a car stops, she presents herself as a hitchhiker and asks the driver to go to Nation, a central place in Paris. Thus, Mrs Cheng adapts her strategy to the threat of police arrests. She, along with many of her colleagues, plays catand-mouse with the police. She alters her schedule and solicits when she assumes the police are having lunch or after 7 pm when they are off work. She explains that she only 'takes a walk' in the neighborhood and goes back home to avoid being remarked on as a prostitute. In this respect, Chinese prostitutes develop collective know-how in a situation where the tactics of seeing and being seen are very important. Far from dismantling Chinese prostitution, police control seems to have helped it become more organized. Key tools utilized for this are cell phones and massage salons. During our interviews, our interviewees' cell phones never stopped ringing. We observed the women answering with the few French words they knew -which were nonetheless sufficient to make an appointment. By having an active cell-phone number, they made themselves available to loyal clients. In a context of police repression, cell phones allowed them to limit the time they spent 'outside' and yet generate enough income to 'survive' (on the condition that they limited their expenses). Another option is now being developed with the opening of massage salons in response to an increasing interest in oriental therapies. All the women we interviewed consider this option as a good alternative to the street. For example, Mrs Wang took an intensive course of massage techniques when she went back to China, allowing her to work at a friend's salon in the 20th arrondissement. She explained how she offered extra sexual services, which enabled her to ask for an extra 35 euros directly from the client, whereas a normal massage only costs 10 euros. However, once again, the police are not taken in, and she no longer offers this service, as her friend began to fear her business would be closed down.

One negative side-effect of the police targeting prostitution is the increased isolation of these prostitutes. Like many illegal migrants, whatever their nationality, our interviewees had no access to protection from the law, social services or other agencies. This situation creates a well-known vicious cycle: for fear of being deported, migrants are dissuaded from asking police or justice services for protection and are thus victims of many crimes that go unreported. Women are put at even greater risk as this situation tends to increase dangerous behaviors. For example, many interviewees said they do not go to hotels anymore but go to the client's home, where they are isolated and cannot control their environment. An overwhelming fear in these cases is of being sequestered, as Mrs Wang experienced, or of discovering there are several men at the client's house. Another negative side-effect is the tendency not to have safe sex, as the presence of condoms is considered sufficient proof to justify being arrested for prostitution. They even hesitate to use the condoms distributed by NGOs. Mrs Cheng said she had been questioned by the police several times immediately after she went to the NGOs' buses.

An informal collective organization

Confined to the margins of society, prostitutes have to organize in order to solve the problems they face on the street. Mrs Cheng underlined the solidarity that exists between colleagues and the development of collective know-how. In the street or at their client's home, safety is never guaranteed. Prostitutes have to be aware of numerous situations such as violence, robbery and sexual assault. Examples given to us by our interviewees include being robbed of their handbag with all their money after a trick, being sequestered, beaten, and forced into sexual acts such as unprotected or anal intercourse. Chinese prostitutes in Paris therefore go out in groups of two or three and they form a protective network by keeping an eye on each other. If a colleague is missing or gone for too long, they warn one another. They also use this system when a colleague gets arrested.

Sharing information is the only way they have to protect themselves from danger and threats, and these women regularly share their various experiences and misfortunes with each other. For example, they pass on the physical description of the cars or the license-plate numbers of clients who do not pay, who are violent, or are under-cover policemen. Mrs Shu, an illegal migrant, explained to us that her friend called to warn her of the arrival of the police so that she was able to escape and hide from them. She says it often happens and that she usually goes to the second floor of a shop, where she waits for another call to tell her they are gone. Even though the women believe this mutual help is effective, there are many gaps. Some newcomers suffer from this imperfect protective network, as for example Mrs Liu, who did not hear about a serial rapist and became one of his victims. The information was not widely shared, and the four other victims did not report

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Northern Chinese women in Paris 639 being raped to the police for fear of arrest. Furthermore, it is difficult to teach experiential knowledge and intuition. One has to learn on the job: 'Once you have been trapped several times, you do not do it anymore', Mrs Cheng explains. For example, it was only after she 'had done it for free', she learned that she had to insist on being paid before the encounter.

Controlling an unstable environment

Interestingly, our interviewees tended to consider that they controlled the situation and that they were the ones who decided what they wanted to do or not with a client. Experience allowed them to develop the ability to size up their clients, a skill that is essential for avoiding danger and threats. Mrs Cheng was very proud of her 'eye' and explained that, when she is given the glance by a potential client, she has only a few seconds to decide whether to accept or to decline: 'You have to decide if it is a person you can trust, and make sure that there is no policeman or anyone you know in the area.' Mrs Liu and Mrs Liao added that you have to 'trust your first impression'. The prostitutes we interviewed developed a typology of clients. They preferred 'true French' over immigrants, despite the fact the latter are more numerous in the areas where they work. They also said they preferred men in their 40s or older. Mrs Liao explains that 'with older men, one is more unencumbered'. They feared younger men, whom they consider to be potentially more violent. This hierarchy between customers conveys many stereotypes about other cultures. French men are supposed to be nicer and more thoughtful, whereas Black or Arab men are seen as 'thieves'. 6 For example, Mrs Wang explained that Arab men often 'do it, but do not want to pay'. As for Chinese men, our interviewees systematically refuse to have intercourse with them. Mrs Liao explained that it is too embarrassing. These men would ask her to justify her work or make comments she did not want to hear. Mrs Cheng said that she 'would not do it, even for 100 euros', in order to keep her circle of friends and her family from discovering her real activity. Thus it appears that soliciting Chinese men would reactivate a gendered social control they insisted they had got rid of. In this regard, engaging in sexual relations with men from other cultures allowed these women to escape their own cultural gendered norms. 7 Their inability to speak proper French, usually considered a handicap, became an advantage in this situation, as they could not spend time talking or having to justify their actions. Mrs Wang explained that 'doing foreigners (non Chinese) is a bit better, it is quicker'.

It should be noted that this hierarchy between customers is only an ideal, as these women are not in a position to choose their clients. Women who 'are not young and pretty', as they describe themselves, do not find customers that easily. They are not solicited by the customers from what they consider the best categories. As Mrs Liao puts it: 'French men of higher social categories choose the prettiest ones and the ones who know how to look their best. You see, I am fat, I don't look for French men, I go for Turkish or Moroccan men.' In that respect, most Chinese prostitutes are confined to a relatively poor clientele, often composed of immigrants, workingclass Frenchmen or retirees. The price of a trick -usually oral or vaginal intercourse -ranges from 20 to 45 euros. 8 Chinese women are therefore frowned upon by other prostitutes, who blame them for being 'unprofessional' by bringing prices down.

Competition is quite fierce, as there are more and more Chinese migrants who have no other option in France than prostitution, in a rapidly evolving situation. On the one hand, our interviewees reported the arrival of prostitutes from other parts of China -particularly Jiangxi. These women are reported to enter prostitution as soon as they arrive. 9 On the other hand, there is a tendency for professionalization, with the presence of younger and more elegant Chinese women, often with legal status, who seek a wealthier clientele. They operate in a more 'traditional' way, similar to professional prostitutes of other origins, and work in wealthier areas of Paris. This variation in Chinese prostitution patterns may influence the practices of Northern Chinese women, as competition is accompanied by police repression and a diminishing number of clients. 10 Many women complain of the diminishing number of customers and say they can spend several days without any business. Thus, their already modest income is decreasing. Mrs Wang explained that she used to earn 150 euros a day a few years ago, but that this is not the case anymore. Nevertheless, to these women, prostitution remains a means of making 'quick and easy' money. 'In a few minutes you have 30 euros in you pocket, that still is more than one day as a maid', Mrs Cheng added.

Making the best of it

The extremely precarious situation that Northern Chinese women endure when they arrive in France, along with the shame they feel towards their forced decision to enter prostitution, drive them to develop know-how and tactics for surviving in everyday situations. They adapt and make the best of the worst. The women we interviewed learned how to work with clients despite their lack of linguistic knowledge; they also became skilled at quickly sizing up a dangerous situation, and at negotiating the price of a trick without fully understanding what their customers might be saying.

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They learned to organize in peer-networks. In this respect, they developed the skills to be able to work discreetly, attempting to avoid danger and threats, especially police checks. They tried to act like ordinary citizens and blurred the stereotypes attached to prostitution. For these Northern Chinese women, prostitution appears to be a way of taking control of their destiny as illegal migrants. Money -which is hardly mentioned -is the best way to bridge the gap between the reality they face in France and the dreams they had before departure. Their activity, which is quite dangerous and which they are ashamed of, eventually allows them to make the money they would not have earned in the Chinese ethnicized labor market. Highlighting such a phenomenon allows us to document the ability of Northern Chinese prostitutes to develop skills that are shaped by their illegal condition as migrants and that are seldom talked about, as the focus is usually on their otherwise desperate situation. 2. We have very little quantitative information on those who enter France with visas and overstay illegally. Nevertheless, some data from an association dealing with migrants show that 70% of 826 Northern Chinese migrants are women (Gao & Poisson, 2005). We should also note that class classifications are notoriously difficult for China, and should not be given great analytical weight; these women are educated and held jobs that allowed them to enjoy a respectable amount of social status or at least status security.

Florence

  Lévy, CECMC, EHESS -MAPS (Center for the understanding of social processes), University of Neuchâtel, 27 Faubourg de l'Hôpital, 2000 Neuchâtel, Switzerland. [email: florence.levy@unine.ch] Marylène Lieber, MAPS (Center for the understanding of social processes), University of Neuchatel, 27 Faubourg de l'Hôpital, 2000 Neuchâtel, Switzerland. [email: marylene.lieber@unine.ch] Notes * Our warm thanks go to Gabrielle Peterson and Ellen Hertz for their help in revising our English translation. 1. This article is based on more than 40 formal and informal interviews with, as well as on participant observation of, Chinese women living in Paris -prostitutes or not. Conducted between 2000 and 2007, these formal interviews and observations are part of Florence Lévy's PhD research on the migratory projects of Northern Chinese women in Paris. As a volunteer translator, she worked for one year with social workers in the area of Aids prevention for Chinese drug addicts and prostitutes in the Hôpital Maison Blanche. Informal interviews and observation were also part of Marylène Lieber's associative experience of teaching French to Chinese migrants in Paris for three years with the Association Pierre-Ducerf. From 2003 to 2004 she also served as a volunteer translator with Médecins du Monde in the area of health prevention, focusing on Chinese drug addicts and prostitutes.

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642 Social Science Information Vol 47 -no 4 3. See Lévy (2005) for the reasons why these women embark upon a migratory project. 4. For more about the ways local men regard foreign women, see Weber (2007). 5. As prostitutes are usually considered to be of no value, they are often the targets of violence. This phenomenon is not specific to Chinese prostitutes (see Pourette, 2005).

6. Concerning stereotypes of men from other cultures see Brennan (2002) and Hung (2002). 7. On this subject see Tabet (2005). 8. The old women from the Maghreb who prostitute themselves in immigrant shelters or on the street, as described by Nasima Moujoud, ask for even lower prices (Moujoud, 2005). 9. We have not documented this new type of prostitution. It should be noted, however, that making this distinction between themselves and women from other parts of China who come to France to prostitute themselves allowed our interviewees to underline the fact that they did not initially want to go into prostitution, and to present themselves as respectable rather than 'easy' women. 10. This is the case for all categories of prostitutes (see Amaouche, 2008).