Armelle Garcia

Alya Hammami

Lucie Mazellier

Julien Lagneau

Nicolas N Darcel

Suzanne Higgs

Olga Davidenko

Social modeling of food choices in real life conditions concerns specific food categories

Keywords: social modeling, food choice 26 27 social modeling, food choice OR: Odd Ratios, CI: Confidence Intervals

Eating is a complex social event, and the social context during a meal can have multiple influences on 29 food intake. For instance, it has been demonstrated that the quantity of food consumed increases 30 when eating with familiar others compared to eating alone, which is known as the social facilitation of 31 eating (de Castro & Brewer, 1992). However, not only the mere presence of others, but also their 32 consumption can have an impact on intake. Indeed, it has been shown that individuals adjust the 33 amount of food eaten to the quantities consumed by their commensals (Vartanian et al., 2015). This 34 phenomenon is called social modeling and involves for 35 instance as an indicator of the appropriate amount of food to consume in a given situation. Social 36 modeling appears to be very robust because it has been observed in both men and women (Cruwys et 37 al., 2015) (with some evidence of a stronger effect for women (Herman & Polivy, 2010)), when eating 38 with both familiar and unfamiliar partners (Cruwys et al., 2015;Kaisari & Higgs, 2015;Salvy et al., 2007;39 Vartanian et al., 2015), and independently of weight status (Rosenthal & Marx, 1979) and state of 40 hunger [START_REF] Goldman | Is the effect of a social model on eating attenuated 357 by hunger[END_REF]. Additional studies have demonstrated that social modeling can occur 41 even in the absence of others, when participants are provided information regarding the quantity of 42 (Robinson, Benwell, et al., 2013;43 Vartanian et al., 2013). In such studies, a norm of consumption is established via descriptive norms, 44 which can be indirectly conveyed, e.g. via the presence of empty packaging, or conveyed via messages 45 that report the consumption patterns of a majority of individuals (social-norm based messages). 46

While social modeling of food intake is well established, less is known about social modeling of food 47 choices. Two reviews (Cruwys et al., 2015;Robinson, Thomas, et al., 2013) examined the literature on 48 the impact of social modeling on food intake and choices, and both concluded that the available data 49 is insufficient to draw conclusions about the robustness of the effect on food choices. Indeed, among 50 69 studies reviewed by Cruwys, Bevelander and Hermans (2015) on social modeling, only 11 examined 51 modeling of food choices, among which 8 succeeded in observing the phenomenon. However, 3 52 instance, Pliner and Mann (2004) reported social modeling of food intake but not of food choices. 54

These authors suggested that food 55 because individuals feel surer about their food likes and dislikes than the appropriate amount of food 56 to consume in a given situation. 57 Pliner and Mann (2004) were also interested in the impact of food healthfulness on modeling, and they 58 observed social modeling of intake for unhealthy (high energy density) cookies but not for healthy 59 (low energy density) ones. To date, little is known about the strength of social modeling effects on 60 food items because the majority of studies have been done using high energy density food 61 items and only a small number of studies have focused on modeling of low energy density food items. 62

In a study by Hermans et al. (2009), social modeling of quantities of low energy density food 63 (vegetables) was observed, but investigations of the social modeling of food choices of low versus high 64 energy density food has been limited. Robinson and Higgs (2013) found that participants were less 65 likely to choose low energy density 66 choices, than when eating alone or in the (Robinson 67 & Higgs, 2013). Thus, social modeling of food choices was observed, but this influence was only present 68 69 the 70 provision of a descriptive norm (empty packaging) before having to make their own choice. 71

Participants were more likely to choose the snack they believed others had chosen, both in the 72 s (Burger et al., 2010). 73 One feature of these studies is that they were conducted in a laboratory setting, which leaves open 74 the question of whether the modeling of food choices occurs in real-life situations. To date, one study 75 investigated modeling of choices in real life conditions, but this study was focused on modeling of 76 vegetarian versus non-vegetarian dishes (Christie & Chen 2018). Further research is needed to better 77 characterize the effect of social modeling of food choices in real-life conditions, especially for meals 78 composed of a broad range of food items including low and high energy density food items. In addition, 79 there are other factors which could impact modeling effects in real life settings that are often not 80 present in laboratory studies. For instance the majority of studies conducted in the laboratory included 81 subjects paired with strangers, whereas in real life situations people are likely to eat with familiar 82 others. To date, only a few studies have examined modelling among familiar participants, but the 83 results suggest that modeling may occur both with familiar and unfamiliar individuals (Cruwys et al., 84 2015;Kaisari & Higgs, 2015;Salvy et al., 2007;Vartanian et al., 2015). Further investigations are needed 85 to confirm these preliminary results and examine whether familiarity moderates modeling observed 86 in real life settings. 87

The aim of the present study was to determine whether modelling of food choices can be observed in 88 a real-life setting (a university self-service restaurant), examine whether modelling exists across a 89 range of food categories (defined using consumer perception of the nutritional quality of the items) 90 and whether familiarity with the person that serves as a model moderates any effects. 91 92

Methods

94

Restaurant Venue 95

The study took place at the employee restaurant of a university campus (Paris, France). The restaurant 96 serves almost 500 clients per day for lunch service. Ethical approval for the study was obtained from 97 the ethics committee of Paris-Saclay University (registration number CER-Paris-Saclay-2019-016). Data 98 collection took place on two Thursdays (one of the busiest days of the week) during spring, from 12pm 99 to 2pm. Clients were able to choose a main dish plus two additional items for their meal, meaning they 100 could choose one starter and one dessert, or two starters or two desserts. The price of the meal 101 remained the same, no matter which items were chosen. Food choices varied from day to day but 102 (such as pasta or potato salads with cheese), meat or fish, and cold meats. The desserts included a 104 variety of items including dairy products (different types of yoghurts, dairy desserts and cheeses), but 105 also fresh fruits, fruit salads, fruit compotes and different type of pastries. Finally, the main dish offered 106 usually included a choice between fish or meat and one or two types of sides (starches and vegetables). 107

We decided to focus our analysis on the choices of starters and desserts only, because these courses 108 offered a higher diversity and choice than the main dishes at the restaurant. 109

We sub-categorized starters into salads, mixed starters, and cold meats, and desserts into fruit, dairy 110 products and pastries (more details about food items included in subcategories are available in 111 supplementary file 1). These food categories were decided upon according to their perceived 112 healthiness by consumers. This perceived healthiness was assessed via an online questionnaire 113 completed by 118 individual (mean age of 42 years old). Participants were asked to rate each item 114 from 0 (unhealthy) to 8 (healthy). Detailed results are available in supplementary file 2. 115 116

Data collection procedure 117

Upon arrival at the restaurant, clients had to follow a linear queue along the buffet to choose their 118 lunch items. This linear configuration of the buffet was a crucial criterion for the choice of venue for 119 the study because it ensures that each client had to follow the same person all along the buffet. The 120 clients first had to choose a starter, then a dessert, and last the main dish. Two experimenters were 121 positioned at the cash register, behind the cashier, from where they had a clear view of the meal trays, 122 but were not seen by the clients in the queue so as not to influence their choices. The choices of each 123 client were recorded by the two experimenters. A third experimenter distributed individual 124 questionnaires to each client after the cash register. The aim of these questionnaires was to collect 125 demographic and contextual information from the clients. Finally, a fourth experimenter collected 126 individual questionnaires at the exit of the restaurant. An identification number was associated with 127 identify who was following who. We did not record data on the clients who did not follow anyone in 129 the line (which typically happened at the very beginning and the very end of the lunch service). 130 131 Questionnaires 132

Two questionnaires were used during data collection. First, a food choice questionnaire was completed 133 by the experimenters for each client upon their check out at the cash register. This questionnaire 134 contained all food item options that were on sale the day of each data collection session. Second, the 135 individual questionnaire was distributed to the clients for self-assessment after they paid for the meal. 136

The individual questionnaire included questions about age, gender and Body Mass Index (BMI) of the 137 participant, and contextual information: whether or not they knew the person ahead in the queue, 138 whether they ate with that person, whether they were following a specific diet involving food 139 exclusions, and what were their usual food choices at the canteen (whether they usually take a starter, 140 a dessert, or both) d their own choices. 141 142

Study population 143

The food choices of 797 clients were collected over two days of data collection separated by one and 144 a half months. We excluded 110 individuals who reported following a specific diet involving food 145 exclusions e.g. vegetarian. We also excluded observations of 141 individuals who did not return the 146 individual questionnaires, or returned incomplete questionnaires. The final sample comprised 546 147 individuals. 148 149 Statistical analysis 150 not chosen as 0). Binary logistic regressions were used to analyze whether the probability of choosing 152 an item from a specific food category or subcategory was associated with the choice of the person 153 ahead in the queue for this same food category or subcategory. 154

The models were adjusted for the age, sex, and BMI of individuals. The models were also adjusted for 155 contextual factors such as declared usual food choices at the self-service restaurant. We controlled for 156 habitual choices because this is a powerful predictor of eating behavior (Riet et al., 2011). Finally the 157 models were adjusted for the familiarity (whether individuals stated that they knew the person ahead 158 of them in the queue). This allowed us to investigate modeling, independently of the relation of 159 individuals with the previous person and to investigate if the familiarity could impact indiv 160 choices, independently of the choices of the person ahead in the queue. 161

Finally, the possible moderating effect of the familiarity between subjects on modeling has been tested 162 in other models, through the investigation of interactions between the familiarity and the choice of 163 the person ahead. 164 R Studio version 1.1.463 (RStudio Team, 2016)

Population characteristics 170

Of the 546 individuals observed, 333 (61%) were men and 211 (39%) were women (2 individuals did 171 not give their sex). The mean age was 40.2 +/-13.1 years old and the estimated mean BMI was 23.5 172 +/-3.5 kg/m 2 . The sample was composed of university employees including PhD students (21%), 173 permanent research staff (44%) and administrative and support employees (27%) (8% of the sample 174 did not state their profession).

Food choices 177

We observed that 39% of the population chose at least one starter, 93% chose at least one dessert, 178 and 35% chose both starter(s) and dessert(s). This compares with 54% of the sample who stated that 179 they usually take a starter, 90% a dessert, and 44% both (Table 1 with mixed starters accounting for 28% of choices and cold meats accounting for 16% of choices. 185 Among desserts, fruit were the most chosen category with 43% of choices, then came pastries with 186 32% of choices and last dairy products with 25% of choices. 187 188 Relationship with the person ahead in the queue 189 65% of the sample reported they knew the person ahead in the queue, and 62% declared that they ate 190 at the same table. Among the 65% of individuals who declared knowing the person ahead in the queue, 191 93% also declared that they ate with this same person. Due to the high association between two 192 variables, we only used the knowledge of the person ahead in the queue as a covariate in the model. 193 194 Awareness 195 The vast majority of the population (91%) reported that in making their own choice, they were not 196 197 the unequal distribution of this variable, we decided not to use it as a covariate in order to adjust our 198 models. 199 200

Social modelling of food choices 201

Starters 202

There was a significantly higher probability of taking a starter if the person ahead in the queue also 203 took one compared to when the person ahead in the queue did not take one (OR= 1.65, CI= 1.06-2.57, 204 p= 0.03). Regarding subcategories of starters, individuals had a significantly higher probability of taking 205 a salad (OR= 1.78 CI= 1.08-2.93, p= 0.02), or a mixed starter (OR= 2.98, CI= 1.42-6.05, p<0.01) if the 206 person ahead in the queue also took one, which was not the case for cold meats (OR= 1.89,p= 0.34). 208

Desserts 210

The probability of taking a dessert was not significantly related to whether the person ahead in the 211 queue did or did not take one (OR= 1.28,p= 0.73). Regarding desserts subcategories, 212 none of the choices was significantly influenced by the choices of the person ahead in the queue for 213 those subcategories (fruit: OR= 0.97, CI= 0.69-1.38, p=0.88; dairy products: OR=1.26, CI= 0.84-1.87, 214 p=0.27; pastries: OR= 1.42, CI=0.97-2.07, p= 0. 07). 215 216 Other variables explaining food choices 217 Declared 218 d for desserts in general as well as for fruits and pastries. Complete 219 statistical results of each model are available in supplementary files 3 for starters and 4 for desserts. 220

Familiarity and modeling 222

We found no significant interaction between the familiarity and the choice of the person ahead in the 223 queue in any of the categories and subcategories (Table 3) We observed that choice of starters, but not desserts, was influenced by the choice of the person 230 ahead in a queue in a restaurant setting. We also found that whether or not participants knew the 231 person ahead in the queue had no influence on whether or not modelling was observed. These results 232 are significant because they demonstrate social modeling of food choices in a real-life restaurant 233 setting, which has only been observed in one previous study of food choices of vegetarian versus non-234 vegetarian main dishes (Christie & Chen, 2018). Taken together, these findings suggest that modeling 235 of food choices is a robust phenomenon that exists outside of the laboratory. 236

237

We observed a modeling effect for starters and two of the starter subcategories (salads and mixed 238 starters), but not for desserts and none of the dessert subcategories. One reason why modeling was 239 a dessert whereas only 54% reported usually taking a starter. In situations where people have clear 241 eating routines and/or strong pre-established preferences, social modelling is less likely to be observed 242 (Cruwys et al., 2015). Pliner and Mann (2004) observed social modeling for intake of 243 palatable/unhealthful cookies but not for unpalatable/healthful cookies. In addition, these authors did 244 not observe any influences of informational social norms on choice of cookies (participants chose the 245 palatable over the unpalatable cookies regardless of social information). These authors suggested that 246 one reason for these findings might have been because the participants were sure of their preference 247 for cookies and so did not look to others to guide their choices. In the present study it is possible that 248 choice of dessert was less susceptible to social influence than was choice of starter because the 249 participants were sure of their preference for these items. An additional explanation why choices of 250 starters were modeled, but not choices of dessert, may be the fact that the starter was the first item 251 to be chosen and this may have made the starter more visible on the tray of the person ahead in the 252 queue as it was the only item added on it. In addition, because the starter was the first item to be 253 chosen, choice of starter was not restricted by already having chosen other items. The choice of the 254 dessert (which was the second item to be chosen) may have been in part influenced by the choice of 255 the starter for the sake of having a balanced complete meal and makes it more susceptible to external 256 influences. 257

For the choice of starters, we observed social modeling of choice of the salads and mixed starters but 258 not for the cold meats. There are at least two potential reasons for this finding: 1) the cold meat 259 starters were not chosen very often and so it may be that there were insufficient observations for 260 modelling to be evident but it is also possible that 2) the participants were also more sure of their 261 preference for the cold meats than they were of their preference for the salads/mixed starters and so 262 while choice of the latter was susceptible to social influence the former was not. 263 264 (salads). This is significant because to date, the majority of studies on social 266 modeling have examined modelling of high energy foods and the effect of social context on the choice 267 of low versus high energy food items during a meal remains poorly studied. Robinson and Higgs (2013) 268 reported findings that differ somewhat from the present results. They found that participants choosing 269 from a buffet in the presence of an 270 and consume a low-energy-dense food item than when choosing alone or in the presence 271 eater, suggesting that the presence of an 272 consume low-energy-dense foods (Robinson & Higgs, 2013). This study was conducted in a laboratory 273 setting and the fact that the participants knew they were taking part in an experiment may have 274 affected their responses. As argued by Robinson and Higgs (2013), it may have been that in the 275 -alo confederate condition, there was a social norm operating 276 to encourage the choice of at least some healthier items (perhaps because they thought their food 277 intake was being monitored), but this this norm may have been under 278 confederate condition, leading participants to abandon the The present results are 279 encouraging in suggesting that modeling could have a positive impact on healthier food choices in a 280 real life setting. 281 282 Social modelling of starter choice was unaffected by whether or not the participant reported knowing 283 the model. In other words, we observed modelling of food choices both under conditions of familiarity 284 with the model and when the model was not known to the participants. This is an important finding 285 because the majority of studies to date on modelling have involved testing of strangers. Reviews by 286 Cruwys and colleagues (2015), and Vartanian and colleagues (2015) found modeling in studies of 287 children eating with familiar models such as parents or teachers but to date only a few studies have 288 reported social modeling in pre-existing friendship groups of adults (Howland et al., 2012;Kaisari & 289 within friendship networks may underlie the social transmission of food choices (Pachucki et al., 2011). 291

Ninety-six percent of our population reported that they were no 292 making their own choices. Other research has found that people generally report being unaware of 293 social influence when it comes to food choices, although it may be that people are aware of social 294 influence under some circumstances, but are motivated to deny it (e.g. Spanos et al., 2014;Vartanian 295 et al., 2008, Robinson & Field, 2015). One explanation for a lack of awareness is that 296 on eating could be operating on an automatic level. Indeed it has been argued that an automatic 297 mimicry process may underlie modelling at least in part (Bevelander et al., 2013;Hermans et al., 2012;298 Higgs & Thomas, 2016;Huh et al., 2014). In their review, Cruwys and colleagues (2015) concluded that 299 the evidence suggests that while modeling can be automatic, it is also accessible to conscious control. 300

For example modeling has also been observed in studies using descriptive social norm-based 301 messages, which is unlikely to be explained by automatic mimicry process (Cruwys et al., 2015). More 302 research is needed to better understand mechanisms underlying social modeling. 303

This study has some limitations. Our study was observational and did not involve manipulation of food 304 choices, and so we cannot draw conclusions about causal effects in relation to modelling. Moreover, 305 the potential role of confounding factors cannot be ruled out, such as the order of choice of the 306 different items for example as discussed above. However, observational studies yield complementary 307 data and lend external validity to evidence obtained from laboratory studies conducted under 308 controlled conditions. Another point is that we recorded food choices, but we did not measure the 309 quantity of food actually consumed. Future work could examine whether modelled foods are more or 310 less likely to be wasted than are non-modelled foods. 311

This research has implications for interventions on healthy eating behavior. For example, it may be 312 possible to encourage choice of healthier menu items using social influence. Studies have already 313 succeeded in increasing the purchase of vegetables/salads in restaurants (Mollen et al., 2013, Thomas 314 social-norm-based messages may be more effective than health-based messages (Collins et al., 2019;316 Robinson et al., 2014), especially when using descriptive norms (information about what others are 317 eating) rather than injunctive norms (information about others approve of) (Mollen et al., 2013;318 Robinson et al., 2014;Stok et al., 2014). Future studies could examine whether social-norm-based 319 message can also be applied to a wider range of healthier items and in a wider range of field settings. 320

In conclusion, we observed modelling of food choices in a real life setting but our data suggest that 321 choices in some food categories, such as starters, may be more susceptible to social modeling than 322 others, such as desserts, possibly because choice of dessert is habitual. Finally, we found that modeling 323 occurs both between familiar and unfamiliar participants, which suggests that social norms could be 324 used to promote healthier eating in a range of settings including friendship groups.

Introduction

Eating is a complex social event, and the social context during a meal can have multiple influences on food intake. For instance, it has been demonstrated that the quantity of food consumed increases when eating with familiar others compared to eating alone, which is known as the social facilitation of eating (de Castro & Brewer, 1992). However, not only the mere presence of others, but also their consumption can have an impact on intake. Indeed, it has been shown that individuals adjust the amount of food eaten to the quantities consumed by their commensals (Vartanian et al., 2015). This phenomenon is called social modeling and involves for instance as an indicator of the appropriate amount of food to consume in a given situation. Social modeling appears to be very robust because it has been observed in both men and women (Cruwys et al., 2015) (with some evidence of a stronger effect for women (Herman & Polivy, 2010)), when eating with both familiar and unfamiliar partners (Cruwys et al., 2015;Kaisari & Higgs, 2015;Salvy et al., 2007;Vartanian et al., 2015), and independently of weight status (Rosenthal & Marx, 1979) and state of hunger [START_REF] Goldman | Is the effect of a social model on eating attenuated 357 by hunger[END_REF]. Additional studies have demonstrated that social modeling can occur even in the absence of others, when participants are provided information regarding the quantity of (Robinson, Benwell, et al., 2013;Vartanian et al., 2013). In such studies, a norm of consumption is established via descriptive norms, which can be indirectly conveyed, e.g. via the presence of empty packaging, or conveyed via messages that report the consumption patterns of a majority of individuals (social-norm based messages).

While social modeling of food intake is well established, less is known about social modeling of food choices. Two reviews (Cruwys et al., 2015;Robinson, Thomas, et al., 2013) examined the literature on the impact of social modeling on food intake and choices, and both concluded that the available data is insufficient to draw conclusions about the robustness of the effect on food choices. Indeed, among 69 studies reviewed by Cruwys, Bevelander and Hermans (2015) on social modeling, only 11 examined modeling of food choices, among which 8 succeeded in observing the phenomenon. However, 3 studies did not find significant modeling effect (Hendy & Raudenbush, 2000;Pliner & Mann, 2004). For instance, Pliner and Mann (2004) reported social modeling of food intake but not of food choices.

These authors suggested that food because individuals feel surer about their food likes and dislikes than the appropriate amount of food to consume in a given situation. Pliner and Mann (2004) were also interested in the impact of food healthfulness on modeling, and they observed social modeling of intake for unhealthy (high energy density) cookies but not for healthy (low energy density) ones. To date, little is known about the strength of social modeling effects on food items because the majority of studies have been done using high energy density food items and only a small number of studies have focused on modeling of low energy density food items.

In a study by Hermans et al. (2009), social modeling of quantities of low energy density food (vegetables) was observed, but investigations of the social modeling of food choices of low versus high energy density food has been limited. Robinson and Higgs (2013) found that participants were less likely to choose low energy density choices, than when eating alone or in the (Robinson & Higgs, 2013). Thus, social modeling of food choices was observed, but this influence was only present the provision of a descriptive norm (empty packaging) before having to make their own choice.

Participants were more likely to choose the snack they believed others had chosen, both in the s (Burger et al., 2010).

One feature of these studies is that they were conducted in a laboratory setting, which leaves open the question of whether the modeling of food choices occurs in real-life situations. To date, one study investigated modeling of choices in real life conditions, but this study was focused on modeling of vegetarian versus non-vegetarian dishes (Christie & Chen 2018). Further research is needed to better characterize the effect of social modeling of food choices in real-life conditions, especially for meals composed of a broad range of food items including low and high energy density food items. In addition, there are other factors which could impact modeling effects in real life settings that are often not present in laboratory studies. For instance the majority of studies conducted in the laboratory included subjects paired with strangers, whereas in real life situations people are likely to eat with familiar others. To date, only a few studies have examined modelling among familiar participants, but the results suggest that modeling may occur both with familiar and unfamiliar individuals (Cruwys et al., 2015;Kaisari & Higgs, 2015;Salvy et al., 2007;Vartanian et al., 2015). Further investigations are needed to confirm these preliminary results and examine whether familiarity moderates modeling observed in real life settings.

The aim of the present study was to determine whether modelling of food choices can be observed in a real-life setting (a university self-service restaurant), examine whether modelling exists across a range of food categories (defined using consumer perception of the nutritional quality of the items) and whether familiarity with the person that serves as a model moderates any effects.

Methods

Restaurant Venue

The study took place at the employee restaurant of a university campus (Paris, France). The restaurant serves almost 500 clients per day for lunch service. Ethical approval for the study was obtained from the ethics committee of Paris-Saclay University (registration number CER-Paris-Saclay-2019-016). Data collection took place on two Thursdays (one of the busiest days of the week) during spring, from 12pm to 2pm. Clients were able to choose a main dish plus two additional items for their meal, meaning they could choose one starter and one dessert, or two starters or two desserts. The price of the meal remained the same, no matter which items were chosen. Food choices varied from day to day but were always structured in the same way. Starters included a variety of raw vegetables, mixed salads (such as pasta or potato salads with cheese), meat or fish, and cold meats. The desserts included a variety of items including dairy products (different types of yoghurts, dairy desserts and cheeses), but also fresh fruits, fruit salads, fruit compotes and different type of pastries. Finally, the main dish offered usually included a choice between fish or meat and one or two types of sides (starches and vegetables).

We decided to focus our analysis on the choices of starters and desserts only, because these courses offered a higher diversity and choice than the main dishes at the restaurant.

We sub-categorized starters into salads, mixed starters, and cold meats, and desserts into fruit, dairy products and pastries (more details about food items included in subcategories are available in supplementary file 1). These food categories were decided upon according to their perceived healthiness by consumers. This perceived healthiness was assessed via an online questionnaire completed by 118 individual (mean age of 42 years old). Participants were asked to rate each item from 0 (unhealthy) to 8 (healthy). Detailed results are available in supplementary file 2.

Data collection procedure

Upon arrival at the restaurant, clients had to follow a linear queue along the buffet to choose their lunch items. This linear configuration of the buffet was a crucial criterion for the choice of venue for the study because it ensures that each client had to follow the same person all along the buffet. The clients first had to choose a starter, then a dessert, and last the main dish. Two experimenters were positioned at the cash register, behind the cashier, from where they had a clear view of the meal trays, but were not seen by the clients in the queue so as not to influence their choices. The choices of each client were recorded by the two experimenters. A third experimenter distributed individual questionnaires to each client after the cash register. The aim of these questionnaires was to collect demographic and contextual information from the clients. Finally, a fourth experimenter collected individual questionnaires at the exit of the restaurant. An identification number was associated with each client so that their food choice could be paired to the individual questionnaire, as well as to identify who was following who. We did not record data on the clients who did not follow anyone in the line (which typically happened at the very beginning and the very end of the lunch service).

Questionnaires

Two questionnaires were used during data collection. First, a food choice questionnaire was completed by the experimenters for each client upon their check out at the cash register. This questionnaire contained all food item options that were on sale the day of each data collection session. Second, the individual questionnaire was distributed to the clients for self-assessment after they paid for the meal.

The individual questionnaire included questions about age, gender and Body Mass Index (BMI) of the participant, and contextual information: whether or not they knew the person ahead in the queue, whether they ate with that person, whether they were following a specific diet involving food exclusions, and what were their usual food choices at the canteen (whether they usually take a starter, a dessert, or both) d their own choices.

Study population

The food choices of 797 clients were collected over two days of data collection separated by one and a half months. We excluded 110 individuals who reported following a specific diet involving food exclusions e.g. vegetarian. We also excluded observations of 141 individuals who did not return the individual questionnaires, or returned incomplete questionnaires. The final sample comprised 546 individuals.

Statistical analysis

The choice of each food item, sub-category and category was coded as a binary variable (chosen as 1, not chosen as 0). Binary logistic regressions were used to analyze whether the probability of choosing an item from a specific food category or subcategory was associated with the choice of the person ahead in the queue for this same food category or subcategory.

The models were adjusted for the age, sex, and BMI of individuals. The models were also adjusted for contextual factors such as declared usual food choices at the self-service restaurant. We controlled for habitual choices because this is a powerful predictor of eating behavior (Riet et al., 2011). Finally the models were adjusted for the familiarity (whether individuals stated that they knew the person ahead of them in the queue). This allowed us to investigate modeling, independently of the relation of individuals with the previous person and to investig choices, independently of the choices of the person ahead in the queue.

Finally, the possible moderating effect of the familiarity between subjects on modeling has been tested in other models, through the investigation of interactions between the familiarity and the choice of the person ahead.

R Studio version 1.1.463 (RStudio Team, 2016) was used for data analysis.

Results

Descriptive analysis

Population characteristics

Of the 546 individuals observed, 333 (61%) were men and 211 (39%) were women (2 individuals did not give their sex). The mean age was 40.2 +/-13.1 years old and the estimated mean BMI was 23.5 +/-3.5 kg/m 2 . The sample was composed of university employees including PhD students (21%), permanent research staff (44%) and administrative and support employees (27%) (8% of the sample did not state their profession).

Food choices

We observed that 39% of the population chose at least one starter, 93% chose at least one dessert, and 35% chose both starter(s) and dessert(s). This compares with 54% of the sample who stated that they usually take a starter, 90% a dessert, and 44% both ( Among starters, salads were the most frequently chosen category, representing 57% of starter choices, with mixed starters accounting for 28% of choices and cold meats accounting for 16% of choices.

Among desserts, fruit were the most chosen category with 43% of choices, then came pastries with 32% of choices and last dairy products with 25% of choices.

Relationship with the person ahead in the queue 65% of the sample reported they knew the person ahead in the queue, and 62% declared that they ate at the same table. Among the 65% of individuals who declared knowing the person ahead in the queue, 93% also declared that they ate with this same person. Due to the high association between two variables, we only used the knowledge of the person ahead in the queue as a covariate in the model.

Awareness

The vast majority of the population (91%) reported that in making their own choice, they were not the unequal distribution of this variable, we decided not to use it as a covariate in order to adjust our models.

Social modelling of food choices

Starters

There was a significantly higher probability of taking a starter if the person ahead in the queue also took one compared to when the person ahead in the queue did not take one (OR= 1.65, CI= 1.06-2.57, p= 0.03). Regarding subcategories of starters, individuals had a significantly higher probability of taking a salad (OR= 1.78 CI= 1.08-2.93, p= 0.02), or a mixed starter (OR= 2.98,p<0.01) if the person ahead in the queue also took one, which was not the case for cold meats (OR= 1.89,p= 0.34).

Desserts

The probability of taking a dessert was not significantly related to whether the person ahead in the queue did or did not take one (OR= 1.28,p= 0.73). Regarding desserts subcategories, none of the choices was significantly influenced by the choices of the person ahead in the queue for those subcategories (fruit: OR= 0.97,p=0.88;dairy products: OR=1.26,p=0.27; pastries: OR= 1.42, CI=0.97-2.07, p= 0. 07).

Other variables explaining food choices Declared d for desserts in general as well as for fruits and pastries. Complete statistical results of each model are available in supplementary files 3 for starters and 4 for desserts.

Familiarity and modeling

We found no significant interaction between the familiarity and the choice of the person ahead in the queue in any of the categories and subcategories (Table 3).

Food categories

Familiarity

Discussion

We observed that choice of starters, but not desserts, was influenced by the choice of the person ahead in a queue in a restaurant setting. We also found that whether or not participants knew the person ahead in the queue had no influence on whether or not modelling was observed. These results are significant because they demonstrate social modeling of food choices in a real-life restaurant setting, which has only been observed in one previous study of food choices of vegetarian versus nonvegetarian main dishes (Christie & Chen, 2018). Taken together, these findings suggest that modeling of food choices is a robust phenomenon that exists outside of the laboratory.

We observed a modeling effect for starters and two of the starter subcategories (salads and mixed starters), but not for desserts and none of the dessert subcategories. One reason why modeling was observed for starters but not desserts may be that 93% of participants reported that they usually take a dessert whereas only 54% reported usually taking a starter. In situations where people have clear eating routines and/or strong pre-established preferences, social modelling is less likely to be observed (Cruwys et al., 2015). Pliner and Mann (2004) observed social modeling for intake of palatable/unhealthful cookies but not for unpalatable/healthful cookies. In addition, these authors did not observe any influences of informational social norms on choice of cookies (participants chose the palatable over the unpalatable cookies regardless of social information). These authors suggested that one reason for these findings might have been because the participants were sure of their preference for cookies and so did not look to others to guide their choices. In the present study it is possible that choice of dessert was less susceptible to social influence than was choice of starter because the participants were sure of their preference for these items. An additional explanation why choices of starters were modeled, but not choices of dessert, may be the fact that the starter was the first item to be chosen and this may have made the starter more visible on the tray of the person ahead in the queue as it was the only item added on it. In addition, because the starter was the first item to be chosen, choice of starter was not restricted by already having chosen other items. The choice of the dessert (which was the second item to be chosen) may have been in part influenced by the choice of the starter for the sake of having a balanced complete meal and makes it more susceptible to external influences.

For the choice of starters, we observed social modeling of choice of the salads and mixed starters but not for the cold meats. There are at least two potential reasons for this finding: 1) the cold meat starters were not chosen very often and so it may be that there were insufficient observations for modelling to be evident but it is also possible that 2) the participants were also more sure of their preference for the cold meats than they were of their preference for the salads/mixed starters and so while choice of the latter was susceptible to social influence the former was not.

Regardless of the specific explanation for this pattern of results, we did observe modelling of lower (salads). This is significant because to date, the majority of studies on social modeling have examined modelling of high energy foods and the effect of social context on the choice of low versus high energy food items during a meal remains poorly studied. Robinson and Higgs (2013) reported findings that differ somewhat from the present results. They found that participants choosing from a buffet in the presence of an and consume a low-energy-dense food item than when choosing alone or in the presence eater, suggesting that the presence of an consume low-energy-dense foods (Robinson & Higgs, 2013). This study was conducted in a laboratory setting and the fact that the participants knew they were taking part in an experiment may have affected their responses. As argued by Robinson and Higgs (2013), it may have been that in the -alo confederate condition, there was a social norm operating to encourage the choice of at least some healthier items (perhaps because they thought their food intake was being monitored), but this this norm may have been under confederate condition, leading participants to abandon the The present results are encouraging in suggesting that modeling could have a positive impact on healthier food choices in a real life setting.

Social modelling of starter choice was unaffected by whether or not the participant reported knowing the model. In other words, we observed modelling of food choices both under conditions of familiarity with the model and when the model was not known to the participants. This is an important finding because the majority of studies to date on modelling have involved testing of strangers. Reviews by Cruwys and colleagues (2015), and Vartanian and colleagues (2015) found modeling in studies of children eating with familiar models such as parents or teachers but to date only a few studies have reported social modeling in pre-existing friendship groups of adults (Howland et al., 2012;Kaisari & within friendship networks may underlie the social transmission of food choices (Pachucki et al., 2011).

Ninety-six percent of our population reported that they were no making their own choices. Other research has found that people generally report being unaware of social influence when it comes to food choices, although it may be that people are aware of social influence under some circumstances, but are motivated to deny it (e.g. Spanos et al., 2014;Vartanian et al., 2008, Robinson & Field, 2015). One explanation for a lack of awareness is that on eating could be operating on an automatic level. Indeed it has been argued that an automatic mimicry process may underlie modelling at least in part (Bevelander et al., 2013;Hermans et al., 2012;Higgs & Thomas, 2016;Huh et al., 2014). In their review, Cruwys and colleagues (2015) concluded that the evidence suggests that while modeling can be automatic, it is also accessible to conscious control.

For example modeling has also been observed in studies using descriptive social norm-based messages, which is unlikely to be explained by automatic mimicry process (Cruwys et al., 2015). More research is needed to better understand mechanisms underlying social modeling.

This study has some limitations. Our study was observational and did not involve manipulation of food choices, and so we cannot draw conclusions about causal effects in relation to modelling. Moreover, the potential role of confounding factors cannot be ruled out, such as the order of choice of the different items for example as discussed above. However, observational studies yield complementary data and lend external validity to evidence obtained from laboratory studies conducted under controlled conditions. Another point is that we recorded food choices, but we did not measure the quantity of food actually consumed. Future work could examine whether modelled foods are more or less likely to be wasted than are non-modelled foods.

This research has implications for interventions on healthy eating behavior. For example, it may be possible to encourage choice of healthier menu items using social influence. Studies have already succeeded in increasing the purchase of vegetables/salads in restaurants (Mollen et al., 2013, Thomas social-norm-based messages may be more effective than health-based messages (Collins et al., 2019;Robinson et al., 2014), especially when using descriptive norms (information about what others are eating) rather than injunctive norms (information about others approve of) (Mollen et al., 2013;Robinson et al., 2014;Stok et al., 2014). Future studies could examine whether social-norm-based message can also be applied to a wider range of healthier items and in a wider range of field settings.

In conclusion, we observed modelling of food choices in a real life setting but our data suggest that choices in some food categories, such as starters, may be more susceptible to social modeling than others, such as desserts, possibly because choice of dessert is habitual. Finally, we found that modeling occurs both between familiar and unfamiliar participants, which suggests that social norms could be used to promote healthier eating in a range of settings including friendship groups.

Table 1 :

 1 Numbers and percentages of observed choices compared to declared usual choices.

	). Only 2% (14 individuals) of the

Table 3 :

 3 Moderating effect of familiarity on modeling. Interactions are tested using binary logistic 225

	.

Table 1

 1

	). Only 2% (14 individuals) of the

Table 1 :

 1 Numbers and percentages of observed choices compared to declared usual choices.

Table 3 :

 3 Moderating effect of familiarity on modeling. Interactions are tested using binary logistic regressions.

	*Choice of the person ahead
	(p-value)

The choice of each food item, sub-category and category was coded as a binary variable (chosen as 1,

Acknowledgements 327 This study was funded by the French National Agency for Research (ANR) [ANR-18-CE21-0008]. The 328 authors thank the Crous of Jussieu University that did accept to welcome us in their administrative 329 restaurant. The authors thank David Blumethal for his helpful assistance with the data analysis.

Acknowledgements

This study was funded by the French National Agency for Research (ANR) [ANR-18-CE21-0008]. The authors thank the Crous of Jussieu University that did accept to welcome us in their administrative restaurant. The authors thank David Blumethal for his helpful assistance with the data analysis.

Author Contributions

331

Armelle Garcia was responsible for the study design, data collection and analysis, and writing of the 332 manuscript. Nicolas Darcel, Suzanne Higgs and Olga Davidenko were responsible for the study design 333 and writing of the manuscript. Alya Hammami, Lucie Mazellier and Julien Lagneau participated to assist 334 with the data collection. All the authors approved the final manuscript. 335

The authors declare that they have 336 337

Author Contributions

Armelle Garcia was responsible for the study design, data collection and analysis, and writing of the manuscript. Nicolas Darcel, Suzanne Higgs and Olga Davidenko were responsible for the study design and writing of the manuscript. Alya Hammami, Lucie Mazellier and Julien Lagneau participated to assist with the data collection. All the authors approved the final manuscript.


Ellie Rennie

Michael Zargham

Joshua Tan

Luke Miller

Jonathan Abbott

Kelsie Nabben

Primavera De Filippi

Towards a participatory digital ethnography of blockchain governance

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In decentralised blockchain systems, the ongoing operations, maintenance and improvement of platforms and applications require that people who are unknown to each other arrive at a collective decision. Depending on the platform or application, participants may signal their preference by updating software or sending a transaction to a smart contract. Actions that seem like routine technical or administrative processes can have far-reaching consequences for how digital infrastructures develop and whose interests they serve.

Preceding these actions are conversations that take place on social platforms, also ordered through technical architectures. Such "off-chain" deliberations on social platforms, together with "on-chain" software-based rules and actions, make up what has become known as blockchain governance [START_REF] Werbach | Trust, but verify: Why blockchain needs the law[END_REF], Dgov (Semenchuck 2018), or "DeGov" [START_REF] Buterin | Moving beyond coin voting governance[END_REF]. Identifying effective governance of decentralised blockchain infrastructures is an urgent concern for those involved with implications for the economies that blockchain technologies operate in [START_REF] Mannan | Exit To Community: Strategies for Multi-Stakeholder Ownership in the Platform Economy[END_REF].

Where platform governance of centralised infrastructures calls for greater explainability of automated decision-making to limit harmful outcomes, decentralised communities choose automation to make governance processes manageable and transparent in conditions of low trust. Software may be used to define pre-agreement of processes or actions, reducing scope for error or attack, or improving responsiveness to exogenous influences. In this paper our chief concern is how to understand "governance interactions" within such systems, defined by Kooiman as "solving societal problems or creating societal opportunities; attending to the institutions as contexts for these governing interactions; and establishing a normative foundation for all those activities" (Kooiman 2003, 2).

Focusing on governance interactions makes governance interpretable and analysable (Kooiman 2003), yet in this case requires understanding the roles of, and relationships between, human and non-human actors. We began experimenting with bringing some of the tools of decentralised governance into our own research approach. Aside from ameliorating the challenges of data collection on and across platforms, these tools enabled the co-creation of datasets, illuminated issues and moments that we might otherwise have missed, and helped to turn observation into dialogue. The insight we derive from this is that embedding humanities and social science research inside human-machine entities is not just a means to understand decentralised futures but may help meet their requirement for attentiveness over their own boundaries and dynamics.

We describe our tools and methods for understanding automated futures through a case study of the SourceCred community, an organization using, developing and maintaining open source software called SourceCred. The SourceCred codebase is also used by other decentralised communities for various organisational functions, including reputation and compensation. SourceCred shows how novel forms of measurement (made possibly by automation) can change the nature of online coordination towards new ways of working and belonging for online communities. Ethnography provides a means to observe the processes of "attending to" the algorithm and the experiences of working for and with a system. In the case of SourceCred, governance interactions were inseparable from the values, care and attention of the community's members towards the protocol and each other.

From platform governance to governance surface

To govern, in broad terms, is to constrain the field of action that is available to others [START_REF] Foucault | The subject and power[END_REF]. Governance occurs in both political and non-political spheres and is shaped by systems and tools, evolving "in relation to what is technically possible for it to see at whatever historical moment" [START_REF] Bratton | The Stack: On software and sovereignty[END_REF]. Blockchain infrastructures are governmental in that they are systems for coordination, permitting some actions and limiting others, through what is prescribed in software code. They are also governed in the organizational governance sense -"a sort of constitutional law of the firm" (Colebatch 2014, 309) -entailing rules, standards and policies.

Existing approaches to understanding governance of and by technology provide only limited tools for blockchain governance research due to their focus on centralised infrastructures. The term "platform governance" typically refers either to attempts to curb the power of platforms through regulation (governance of platforms), or to the actions and powers of corporations in setting terms for use (governance by platforms). Scholarship in this area recognises that corporations, even when working within the limits of law, have significant discretion to develop their own rules, which may conflict with other rights and responsibilities (Suzor 2019). Platform corporations derive power through their use of data, including capabilities of pre-emption and surveillance that enact social and political processes through machines of their own creation (Gorwa 2019). At the epistemological level, technology affects how we access knowledge and modifies what we can know and be expected to know (Hong 2020). Andrejevic (2020) observes that automated systems program context, resulting in a "mode of governance that dispenses with processes of subjectification by operating directly on the environment of the individual actors, shaping their conduct by intervening in their surrounding milieux" (18). While the temptation is to see this purely as a problem of transparency, others look instead to the material and ideological influences that may be at play in the use of algorithms (Ananny and Crawford 2018). Researchers from the fields of humanities and social sciences have argued in favour of explainability, which can mean descriptive accounts as well as critical simulation (see Burgess et al. in this issue?). Our focus in this paper is on "explainability to whom?" recognising that decentralised communities seek validation of socio-technical resilience rather than explanations of the mathematical dimensions of systems.

Ethnographic research has focused on the power and practices of users of platforms. Platform governance is complicated and extended by users; tactics applied or overlayed to automated systems may subvert platform logics and create different outcomes. For instance, ethnography has revealed how "boundary work" (Nippert-Eng 2008) is performed by users to delineate and manage social connections, privacy and information sharing within or despite the rules set by platforms [START_REF] Marwick | Networked privacy: How teenagers negotiate context in social media[END_REF]. Others have examined how users can shape platforms through the creation of roles with specific governance functions (Gillespie 2018) or by deploying bespoke, externally created bots to enforce rules, manage spam, or monitor conversations on some platforms (Latzko-Toth 2014). Bots have been associated with problematic governance outcomes when added by external parties as they can alter the terms set by the platform unbeknownst to the user (Jhaver et al. 2019).

Like centralised computing infrastructures, blockchain protocols disrupt political geography, present new challenges for jurisdictions, and have immense amounts of capital associated with them (two trillion USD across blockchains at the time of writing). Automation is also a feature of blockchain platforms, from smart contracts that execute predefined agreements to actions performed by bots beyond the purview and control of most users (such as Miner Extractible Value (Daian et al. 2019)).

However, users of decentralised blockchain systems experience different configurations of power to what is available on centralised platforms; they can maintain custody of assets, control their personal information, and perform peer-to-peer transactions. Some applications on decentralised platforms are 'unstoppable'-meaning immutable codewhere no-one controls the keys to make direct changes and all actions are automated (such as Tornado Cash). At the other end of the spectrum are systems where the power to enact change lies with the community, sometimes with elaborate structures and processes (for instance, MakerDAO). On-chain decision-making in such cases is typically performed using a decentralised autonomous organisation (DAOs), which Hassan and De Filippi define as a "blockchain-based system that enables people to coordinate and govern themselves mediated by a set of self-executing rules deployed on a public blockchain, and whose governance is decentralised-i.e., independent from central control" (Hassan and De Filippi 2021, 1).

There is a growing awareness within blockchain communities that decentralized governance, particularly when tokens are involved, can leave these systems and their constituents vulnerable. Some key issues that confront blockchain projects include: The level of engagement in voting processes (including who votes); the influence of those holding a disproportionate amount of tokens in plutocratic systems; conflict of interest in voting; problems related to identity and reputation (including bots influencing outcomes); collusion; and transparency (Zietz, GEV article). DeGov may also bring new networked forms of accountability. The possibility that members may leave (including copying software to "fork" the protocol) creates pressure on those most invested to be responsive and attune to group needs, although this can be constrained by the costs of exit.

Existing conceptions of platform governance are therefore insufficient for understanding these systems. The question of how they are governed requires paying particular attention to the capabilities and actions of users in relation to the boundaries that are set for and by them. [START_REF] Zargham | Summoning the Money God: Applying Control Theory to Cryptocurrency Monetary Policy[END_REF] have helpfully named this the 'governance surface', which narrowly construed is the set parameters that are subjected to human oversight. Broadly construed, the governance surface is the set of actions made available by a software system that can affect changes to the policies enacted by that software which can include adjusting who has access to set or change parts of the system in the future. By clearly identifying the governance surface, it is possible to analyze the relative stability of a sociotechnical system's properties to governance interactions as well as the system's capacity to adapt to changes in circumstances.

Ethnography for decentralised systems

Some blockchain governance interactions are readily observable through code repositories such as Github or through on-chain transactions. Others occur on sites where people propose, debate and, in some cases, vote on improvements, including Discourse for polls and Gnosis Snapshot for votes. Typologies of governance can be developed by looking at how these various tools are implemented. However, some aspects of blockchain governance are more difficult to trace.

To govern, people must develop a sense of what they hope or expect to see, which they arrive at through learning, mimetic desire (Girard), and agonism (Mouffe). If these aspects of governance are visible anywhere it is on the social channels established by blockchain developers for users. These channels provide for the everyday consumption of information and announcements, and are where people go for assistance, to learn, complain and question. Blockchain communities develop norms, behaviours, and languages to describe themselves within these spaces. Participants may take on moderation roles as they accumulate expertise and status, shaping not only the conduct of people but also the non-human administrators and enforcers of rules.

For decentralised communities grappling with the forces of decentralisation, participation, and automation, ethnography may be the only means by which to surface and analyse unstructured processes, including some vulnerabilities in governance design (either as an input into a mixed methods approach or a standalone exploration of a decentralised community). For instance, while the rules may be visible in Github, activities such as reorganizing the permissions in github or discord (creating private spaces, or defining roles for read/write access, etc) may alter the governance surface, yet be difficult to see, requiring close attention to a project and its community. Research with decentralised communities may also uncover power dynamics within the group, including behaviours and influence on decision outcomes that may have serious consequences for the protocol.

Blockchain projects grappling with decentralised governance are aware that what occurs in informal spaces is part of decision-making processes and their outcomes, evident in efforts to conscript people with experience from other domains to patch gaps in governance and community management. Some projects have commissioned research to better understand what is occurring within Discord communities. For instance, research for UniSwap by Shorin et al (2021) found the forum to be disconnected to governance discussions happening elsewhere and recommended means to address this. However, ethnographers face particular challenges inside these servers. Ethnography typically involves linking up information that is discovered with information that is presented by others. Participants knowingly or unknowingly offer ideas, experiences, and information to the researcher either in passing or through formal interviews. In the process, a dataset is collated, which typically stays with the researcher until the written ethnography is produced. When communication takes place on Discord, logistical and ethical issues can inhibit these workflows. Researchers must deal with high volumes of conversation; when an event is happening, the text chat will move fast, retrievable only through scrolling. Ensuring that participants are aware of the research becomes difficult when people come and go from servers regularly. In creating software that automates consent and data management functions, we also found that ethnography became a more participative process.

Our work is not the first to grapple with these issues in researching digital communities; ethnographers in the fields of STS and communication studies have written of the ambiguity that comes from only being able to see a portion of the action occurring within a system, including what can seem like fleeting interactions [START_REF] Pink | Digital Ethnography: Principles and practice[END_REF][START_REF] Kozinets | Netnography The Essential Guide to Qualitative Social Media Research[END_REF]. As with other studies of online communities, observing governance within blockchain communities entails a shift from "intimate knowledge of face-to-face communities and groups" to looking at communities who may be networked, emergent and changing (Burrell 2009;also Marcus 1995). In online contexts, even longstanding members of these communities -including those who play key roles -may only ever have a partial view into what is taking place. Hine (2020) writes that when undertaking ethnography for the internet, the researcher must accept a "perpetual feeling of uncertainty, of wondering what has been missed, and attempting to build interpretations of events based on sketchy evidence" (pg). To deal with the vast amount of information online, ethnographers have deployed compatible techniques from social science and data science. The tools we created command that researchers interact with a community during the process of data collection and provide the community with visibility over data and data collection processes.

Encountering SourceCred

We approached SourceCred to be our first partner in this endeavour partly because the SourceCred product is used in a number of other blockchain communities to make work traceable and in some cases linked to a payment system. Their CredRank algorithm is adapted from Google's PageRank algorithm (first developed by Larry Page and Sergey Brin at Stanford, Brin & Page 1998) which "computes "importance" of nodes in a graph based on the "importance" of nodes connected to it" (Miyazono 2020; see also Zargham 2019 who was an early contributor to the project). SourceCred's plugins, which are used to gather information, currently work across GitHub, Discourse and Discord.

A token called Cred is minted for each individual contributor according to the calculations of the algorithm. While Cred is non-transferrable, a second token, Grain, is distributed to those with Cred. Within the SourceCred community, Grain is redeemable for USDC (a cryptocurrency pegged to the US dollar) at a rate determined by the community. Those who use instances of SourceCred choose whether or not to issue their own token analogous to Grain (not all do). SourceCred is also interesting from a governance perspective as it has potential to address identity provability problems which are a common challenge in DAOs, in that it creates a default "proof of humanity" by providing visibility over individual contributions across multiple modes of interaction (eg Discord, Discourse, Github, etc). SourceCred developer Seth Benton writes that SourceCred can act as a Sybil-resistant identity tool as "as meaningful contributions over time are difficult for bots to fake" (Benton 2019).

SourceCred's Discord plugin has been a topic of contention within the community at times as it weights contributions that occur in an informal space -exactly the interactions we set out to observe. Measuring Discord contributions and assigning Cred to them was first proposed by founder Dandelion in 2019: "We could have a plugin that reads the history of our Discord server and finds every message with at least one [SourceCred emoji] reaction. Then, it can create a node for that message, with edges connecting the reactors to the message that generated the reaction" (Decentralion, October 2019). A SourceCred emoji was soon created for this purpose and a debate around whether to restrict Cred to GitHub and Discourse ensued. Some argued that Discourse -the platform used for formal proposals -was for more considered contributions and therefore sufficient, while others thought this biased people who were willing to "jump through hoops and self promote" (Decentralion). When our research commenced, community sentiment was falling on the side of recognising diverse contributions and supportive of the Discord plugin as a means to achieve this.

SourceCred was also interesting to us because it was in the process of transitioning its own governance model, moving from a "Temporary Benevolent Dictator" (TBD) to a non-hierarchical structure still to be determined. Various models had been proposed and discussed already, including sociocracy and Indigenous governance systems. The community was also confronting the prospect of a fork, wherein a small group of former SourceCred members had decided that they would copy the codebase of SourceCred and set up a new entity (and forming a new SourceCred DAO). According to the proposer, SourceCred seemed disjointed from the "ecosystem", that experimentation with governance and social activities had become the primary focus, and weightings were favouring Discord contributions. While the community was not opposed to forks in principle (the code is open source), the way it was undertaken was viewed to be hostile by those working on SourceCred at the time and there was a significant risk that existing partners would see the forked version as the legitimate one given the proposer's mention of the Web3 ecosystem. These governance-focused events produced a series of important conversations within the SourceCred community, carried across the various communication platforms used by the group. Our aim was to locate governance interactions of significance. Before discussing this, we first describe Discord and SourceCred's use of it.

Discord and the SourceCred server

The Discord [i] platform launched in 2015 and designed with the sociability of online communities in mind [START_REF] Baguley | Gates and channels: An ANT-oriented approach to understanding fan community behavior and identity on a Discord chat server[END_REF]. Discord is a community center -the beating heart of many blockchain communities. Once a Discord server is created, access is granted via invitation, although this can be a link on a public website. Some channels in a server may be restricted to a core team or only accessible once the user has agreed to abide by certain conditions. Individuals may have multiple Discord accounts in one or more servers, and many use pseudonyms. Those providing technical support and answering questions may be volunteers unknown to the project's core team except through their presence inside the server and related platforms.

One of the attractions of Discord for blockchain communities is its flexible design, allowing admins to configure their server to include channels for specific conversations and bots that automate some tasks. In Discord, bots can serve as upholders of good behaviour and social standards, such as making sure the user understands expectations relating to conduct before being allowed to read or respond to discussions. While the architecture of Discord servers may vary widely, it is the place where people are encouraged to wander in to find information and potentially friends and collaborators.

Although topics are delineated into channels -such as "governance", "support" and "general" -conversations can easily spill across these boundaries. Some blockchain communities are linked, appearing as 'bridges' between servers, reflecting the nested nature of these protocols and projects. The openness of many forums and the open API can also make Discord susceptible to scammers, who can crawl servers for lists of members, sending direct messages to people on those lists. Messaging individuals with questions or to seek their consent for data collection and analysis can be unproductive, time-consuming and easily ignored.

SourceCred uses Discord as its primary place for discussion, announcements and meetings. Social gatherings also take place; a channel where people chat and dance and another for quiet working. Posts that appear on other platforms are often linked to inside Discord, including meeting minutes. More formal deliberation centres on the Discourse site, a platform where people post proposals for others to comment on, but will often be carried over into Discord. Bots are used extensively within the SourceCred Discord server (including bespoke bots), alongside particular non-software systems that have been introduced to make online governance interactions more effective, including the use of American sign language in video calls to signal when one wishes to say something, express sentiment on a discussion point, or to ask for clarification. Use of emojis within the platform are more than responses as the SourceCred algorithm calculates Cred based on emojis. The elaborate system of tools and protocols -human and software -are extraordinary to encounter; beyond an online community, the server is a decentralized workplace and a lab for the development of DAO tools.

Using MetaEth

We created a bot and bridge initially in order to automate the administrative aspects of data collection. Our research with SourceCred officially commenced when an administrator within the SourceCred server ran software to establish a bridge with our own Discord server and to add a bot that we had created: the Telescope. The outcome of these actions meant communication would stretch between Discord servers (SourceCred's and our own), introduce a new space (channel) for SourceCred members, and the collection of information and metadata would commence. The data could be exported into a spreadsheet or carried into a publicly accessible database called Govbase where we record the formal properties of governance (Tan -also add footnote to say we are all involved in Govbase etc). Within the SourceCred server our channel was known as MetaEth (short for ethnography, not Ethereum).

What was initially intended by our team as a simple set of tools to collect key conversations and events from inside a Discord server evolved into a system for networked ideas and inputs sourced across the various applications that SourceCred communicated through. The Telescope bot was set up so that anyone within the Discord server could activate it using the telescope emoji. Once applied to a comment, the bot would then send a message to the project's channel within the researchers' server and ask whether to request permission to add the comment to the dataset (a measure designed to prevent someone spamming the dataset). If a researcher responded yes, the bot would then send a direct message to the author of the comment asking them if they were happy for it to be included, and whether they would like it used anonymously or with attribution to their Discord handle (see diagram). If the author agreed to the comment being included in the dataset, the comment would appear in the approved messages feed, either in an anonymized form or with the associated username, depending on the author's preference. If the author disapproved, the comment would not be added to the dataset (this happened with approximately 20% of requests). Following a suggestion by a community member, we changed the consent process to an 'opt in once' design. In this rendition, an author approves the first comment to be tagged as well as future comments. They continue to be alerted when a new comment of theirs is tagged by the Telescope and may withdraw individual comments even after they have given initial consent.

We found that using a bot to manage the consent process is useful in online communities where members may have arrived after the researchers have introduced themselves to the group. The Telescope bot will relay information about the research and tell the participant who they should contact if they have concerns.

Figure 1: Control flow for the Blockchain Governance Observatory, first iteration

Our presence as a channel within the SourceCred server was not outside community norms; many blockchain communities create a bridge into other projects' Discord servers, providing a feed of announcements or conversation with that external server. The interconnected or nested nature of blockchain infrastructures is visible through these channels. For us, having approved messages appear in a channel inside the community's Discord server was a means to give the community visibility over what data we were collecting.

Comments identified through use of the Telescope can be carried over into Govbase with metadata attached to them, including a timestamp, author (if provided), community name, and context provided by the researcher. The comments can then be used in the development of cases, such as descriptions of events. Handling the comments alongside other data within Govbase makes it possible to gain a clearer picture of the relation between on-chain boundaries and rules and what occurs in the informal governance spaces.

MetaEth in action

People quickly began to grasp how the bot could be used: to go back through significant posts to direct the researcher to historical events or discussions; to highlight when a meeting was happening that might be useful to the researcher; and to direct the researcher to a conversation that had occurred on another platform, including meeting minutes or audio recordings. As expected, some community members used the Telescope bot more than others (taking on the role of "informants") and were more present in our channel discussions. From these engagements we ended up with a dataset that contained important decisions and linked us directly to where a discussion had taken place on a key governance matter. We also achieved clarity over which comments we might quote directly in our research.

The bot also enabled a form of participatory ethnography, with similarities to Tacchi's (2015) Ethnographic Action Research (EAR) method, in which local researchers (working in development settings) collect and share information, assisting researchers to understand the possibilities of technologies while improving their own practice. As comments began to be approved by their authors and appeared in our channel in the SourceCred server, members of the community began discussing the curated list of comments inside the channel. For instance, a member had missed a post when it was first authored and began responding to it, reopening discussion of a particular proposal. In another case, a member of the research team asked a question about a proposed change to the algorithm that had appeared in GitHub, asking whether it had been put to a governance decision (overhauling the "scoring rules" amounted to a significant change in policy with implications for all stakeholders). Members of the development team responded that it was still an experimental design, undertaken at the request of a partner organization who suggested that the PageRank-based algorithm was difficult for their non-technical community members to interpret. A discussion ensued, revealing the change was of 'existential' importance, and the developer leading the change had chosen to tread carefully. While this suggests that there is a role for processes that induce transparency, the contextual detail that emerged from discussion (typical of ethnographic methods) provided insight into the ways that values and concern for others influenced code upgrades at SourceCred. Processes that were not visible from knowing the formal boundaries of governance -including exploration, concern and deliberation that had not yet become visible in formal governance processes -were illuminated in the channel. As one person described it, we had seen a new protocol emerge when it was still being 'turned over by the mycelium'. Similarly, their requests to us, such as the change to the consent process as well as suggestions that we ask more questions, are shifting our research practice.

We also experienced first-hand how automation can obscure some governance processes. If the telescope emoji was applied to a comment in the server that had been generated by another bot, the post would not show up as bots cannot give consent (a fixable problem by assuming default consent). Automation also initially reduced our ability to add context.

After some anxiety at how people might respond to requests that appeared from a bot without any indication as to how it might be used, we altered the system so that the curator could add commentary to a requested message using the reply function in the requested messages feed. Curators were then able to add reflections on the context of the comment or why they would like to include it in the dataset. These comments could be exported to assist with coding the data.

Through these systems we were able to better understand SourceCred itself, including how individuals alter their ways of working and interacting with each other to accommodate the algorithm they work on and for. The SourceCred algorithm needs to know when a contribution has occurred, which is achieved by people recording their own and others' work. This can manifest as expressions of gratitude and support for work in channels called "props" and "didathing". With this comes a heightened awareness of the conditions of their own and each other's labour, how different types of work are weighted, and the legacy of past contributors which never disappears from the tally. Not everything is attributable to the algorithm; the qualities, ethics and concerns of SourceCred arose from the life experiences of individuals involved, some of whom live in geographical proximity to each other (the word 'family' is used often). The same systems would likely generate different outcomes when transposed into a different group and governance system. Suffice it to say that the people and automated systems of SourceCred challenge standard conceptions of workplace hierarchies and how infrastructures are built and maintained. It is precisely these differences that suggest to us that research on decentralized futures needs to look beyond the transparency and explainability of algorithms and towards degrees of attentiveness -towards or because of -automated systems. For instance, to achieve the desired outcome of fair scores, instead of tweaking the tech to feel more fair, SourceCred have at times tweaked their behavioral norms.

While our work on blockchain governance is ongoing, our work with SourceCred has turned it towards looking at the social "closure" of (technical) automated systems. SourceCred the codebase is automated but SourceCred the community is autonomous. And yet the capacity for that autonomous community to self steer so productively is in part attributable to SourceCred code which is a kind of cybernetic sensory perception. The possibilities of blockchain governance and decentralised automated futures lie within this relationship.

Conclusion

In a conference presentation (discovered via a video linked to within the Discord server), the founder of SourceCred, Dandelion, says that SourceCred's version of the PageRank algorithm works in conditions where there is strong community moderation, relying on the attention of the community to prevent it being gamed (ref). While platforms such as Google use obscurity to achieve system security, Dandelion theorised that in decentralised systems security is achieved by people observing each other's actions.

Once our research tools had been added to the SourceCred server, it became apparent to us that research can play a part in generating and focusing attention on issues and questions, possibly enabling the same kind of 'security' in governance processes. While it is too early for us to say whether our approach might impact on governance, participatory and partly automated ethnographic methods can help us understand how decentralized futures are unfolding.

The Telescope bot and its associated channels are, at one level, just a tool -a means for dealing with large amounts of data, managing consent, and communicating with research participants. However, it can also change the research in meaningful ways by surfacing the priorities of a community of users, including their perceptions of key events or issues. It also creates an explicit set of boundaries around approved records, providing those in the forum to know what is on the official record. Falzon (ref) writes that ethnographers "think of their data as a gift from their informants, with all the implications of reciprocity that gift exchange implies" (pg). If anything, the Telescope bot makes the reciprocity implications stronger, establishing expectations for interaction that are more immediate than ethnographers may be used to.

The onus remains with the researchers to understand the context of what is in the dataset, and to question and analyse the data in relation to the broader system. However, the community can be an interlocutor in this process, actively identifying points of interest and seeking responses from the researchers. By using these tools, we also made ourselves a participant in the systems that we seek to study, a product of decentralised human curation and automated administration.