Denisse Albornoz

Maggie Huang

Issra Marie Martin

Maria Mateus

Aicha Yasmine Touré

Leslie Chan

Framing Power: Tracing Key Discourses in Open Science Policies

Keywords:

We would like to thank Swaraj Paul Barooah of the Centre for Internet

education institutions and commercial publishers to commercialize knowledge and in the words of Stuart Lawson, sustain "the very systems it was meant to challenge" [START_REF] Lawson | The politics of open access: Chapter 1[END_REF][START_REF] Neylon | Openness in Scholarship: A Return to Core Values?[END_REF][START_REF] Mayer | From Science 2.0 to Open Science-Turning Rhetoric into Action?[END_REF]. This paper will first conceptualize policy as instruments of "epistemic governance"-the notion that knowledge-related paradigms, values and norms are embedded in policies, which are in turn used to exert power and influence over relevant stakeholders. It will then explain the methodology used to identify the key norms and values present in the framing of Open Science in policies. This will be followed by an overview of the key narratives identified in the data, to then offer a discussion on what these frames tell us about how power is being negotiated and resources being mobilized in and through the concept of Open Science. It will close by reflecting on the need to situate Open Science narratives in the context of global inequality in scientific knowledge production and a call to remain critical of the patterns of domination and dependency that are reproduced through technological trends in scientific production.

Policy as Tools of Epistemic Governance

The ideas that prevail in policy-making processes have the power to influence how resources are channeled, what programs are implemented, and the meanings we attribute to issues of public interest (Alasuutari & Qadir, 2010). In this way, public policies are instruments of what has been dubbed "epistemic governance": the notion that knowledge-related paradigms underlie social systems, and therefore, sustain governance models. Epistemic governance posits that global governance is in large part built on processes of knowledge production and transfer negotiated by stakeholders who belong to and represent a set of interests, relationships and institutional demands [START_REF] Alasuutari | Imageries of the Social World in Epistemic Governance[END_REF]Qadir, 2010, Buduru and[START_REF] Buduru | The Globalized State: Measuring and Monitoring Governance[END_REF]. Under this notion, policies are not neutral, but speak to and evoke these actors' deep-seated values, beliefs and paradigmatic assumptions (Alasuutari & Qadir, 2010;Alasuutari, 2010). Social movement scholars have also investigated the process of articulation and negotiation of ideas by looking at "frame mobilization": how actors communicate their ideals and how they persuade their allies to achieve a set of goals and influence collective action. [START_REF] Kapczynski | The Access to Knowledge Mobilization and the New Politics of Intellectual Property[END_REF], Benford & Snow, 2002, Alasuutari & Qadir, 2010). Alliance building and collective mobilization in these negotiations go hand-in-hand with building consensus on "what is considered virtuous and acceptable", and "what needs to be done on the basis of the current state of affairs" [START_REF] Kapczynski | The Access to Knowledge Mobilization and the New Politics of Intellectual Property[END_REF], Alasuutari & Qadir, 2010). Identifying the values in the narratives is therefore key to understand why Open Science is considered the "virtuous or acceptable" route for knowledge production for these actors-what is there to gain and by whom.

Revealing the underlying assumptions in the frames mobilized in governance models and instruments is key given the highly unequal nature of global relations. Processes of knowledge transfer and consensus building amongst international actors are not neutral -but rather embody the negotiation of unequal power relations that exist in global and local governance. The institutional norms, alliances and values that prevail, set new standards for governance and benchmarks for decision-making ( Van Hulst & Yanow, 2014, Benford and Snow, 2002[START_REF] Mayer | From Science 2.0 to Open Science-Turning Rhetoric into Action?[END_REF][START_REF] Buduru | The Globalized State: Measuring and Monitoring Governance[END_REF]. Knowledge paradigms and benchmarks that triumph in policy are often posited as universally beneficial based on a rhetoric of "objectively" and "rigorously" defined governance indicators, leaving less powerful actors in a position in which they are "expected to calibrate their policy reforms" accordingly (Budaru & Pal, 2010). Given the ideas above, this research conceives of policies as instruments that articulate paradigms that can sustain or relocate power and legitimacy. By investigating how ideas are negotiated in policy making, how they are articulated and whose interests they champion, we seek to illuminate underlying processes of epistemic negotiation in which certain set of ideas and values dominate global conversations.

Methodology 6

Following this logic, and in the context of this research, the data collection process consisted of critically investigating policies as epistemic instruments of discursive dominance. To unpack the key ideas and knowledge paradigms they represent, we sought to identify "narrative frames" or "master frames" in Open Science in policies. This process was carried out in four stages. The first stage 1 involved searching for policy documents, declarations or statements from governments, and other institutions written in English, Spanish and French, with "Open Science" in the title, or with substantial Open Science references within thept of Open Science in the document, namely: definitions, characteristics, benefits of practicing content. 2 The second stage involved identifying characteristic features that framed the concept Open Science, incentives to practice Open Science and main beneficiaries of Open Science. The third stage involved disaggregating the data in relation to these categories, and analyzing them transversally using content analysis methods to identify patterns, similarities and contrasts in the narratives used across regions and institutions. 3

Results: Dominant Frames in Open Science Policies

Who is Shaping the Open Science Agenda 7

In total, we reviewed 49 policies, documents, declarations and statements referencing open science. 4 Open science policies and projects have been produced by stakeholders globally, including by multilateral organisations like Unesco, the G7, and the OECD; as well as by international scientific societies, associations, and industry publishing bodies like the International Council for Science, the International Scientific Association, and the International Association of STM Publishing. The term "Open Science" appears to have the most significant uptake in Europe, with nearly half of the documents sourced produced by European stakeholders. This includes the European Commission who adopted it as an umbrella term for EU-wide research and innovation programmes as well as the government ministries of science, education, and culture in the Netherlands, Finland and Estonia have also produced their own policies, some in direct alignment and endorsement of EU policies; while many academic societies, library associations, university associations, and publishers have done similarly. In North America, open science was referenced in reports and policies by the governments of United States and Canada, in the National Institute of Health, and the Innovation Science, and Economic Development, respectively. In Asia, we found white papers and expert panel reports produced by the Japanese Ministry of Education, Culture, Sports, and Technology. 5 In Latin America, government ministries of science, technology, and innovation in Colombia, Mexico, and Chile released their own national policies; while Latin-American repository network "La Referencia" (consisting of Argentina, Brazil, Chile, Colombia, Ecuador, México, Perú, Venezuela, and El Salvador) also produced a document on open science infrastructures. In Africa, while there were no state policies produced, there have been a number of forums and conferences co-hosted by a variety of stakeholders, including the South-African government academic ministry, the "Academy of Science of South Africa", with the participation of other university institutions like the Association of African Universities and the University of Ghana.

8

Several instances of international and European stakeholders can be seen, contributing via funding and partnership to open science policies and projects in Latin America and Africa. The European Union for example, has taken an interest in infrastructure, completely funding the Sci-GaIA e-infrastructure project in South Africa (who played an instrumental role in the Dakar Declaration of Open Science in Africa); and partially funding the Latin American La Referencia. Research4Life, a public-private partnership between international organizations (WHO, WIPO, among others) and publishers, can also be seen contributing to open science forums in Kenya. While African partners are involved, a significant numbers of presentations and speakers speaking at open science forums and conferences in Botswana appear to be from international organisations like FAO and the Committee on Data of the International Council for Science. While this could be indicative of foreign aid and international support, it suggests policy transfer is likely going from international and northern stakeholders towards historically more marginalised/peripheral regions.

Placeholder for Open Access and Open Data 9

We found that Open Science in policies is being defined interchangeably with or in relation to Open Access and Open Data practices. The Association of European Research Libraries ("LIBER") sees Open Access as the first step to enabling Open Science; Finland approaches Open Access and Open Data as complementary to Open Science, while Chile sees the value of Open Science as a concept but promotes Open Data practices throughout its narrative. Some placed a stronger emphasis on Open Access, such as the African Open Science Platform, Mexico, Japan, and the European Open Science Cloud, which promoted "Open Access by default", and "open-by-default" policies; while others emphasised bolstering the tools, systems and skills to practice Open Data. The European Commissionproduced Mallorca Declaration for Open Science, grants awards and funding to data and software experts; the European Open Science Cloud rewards open data practices by researchers; and the International Association of Scientific, Technical and Medical Publishers (STM) are actively creating new citation principles for data to ensure researchers who share open data get credit for their work. The FAIR data principles, which advocate for data being "findable, accessible, interoperable, and reusable" were embraced by a variety of stakeholders including the EU and STM. These examples are indicative of how even though there is a growing trend in the creation of Open Science policies, the ideas that they are promoting are not new to what has been previously posed by Open Access and Open Data policies and programs. On top of possibly reproducing the limitations of these movements as identified by critical OA scholars, this overall trend indicates that using the term Open Science is possibly more so about popularizing the term, rather than pragmatically adapting the system to open practices outside of what is comprised in OA and OD.

Quality Control through Open Science Standards

10 We found Open Science policies call for standards that guarantee higher quality science, through narratives that promote "improving quality, integrity, citability, reward systems", "quality assurance, enhancement and management", through the development of metrics and indicators for quality control and impact (See policy data sheet). The OECD policy calls for creating clear legal frameworks for the reuse of datasets at the national and international level, while the G7 communiqué calls for international standards for quality that ensures "speed and coherence" in Open Science practice. Meanwhile, STM frames itself and publishers as the "gatekeepers" that protects quality assurance standing between research and the public. There are also references to more "responsible research" and the improvement of "research integrity" via the encouragement of "best practices" in data collection, data management, research ethics, and more. While these standards may enable new forms of collaboration between the institutions and scientists who can meet them, they also risk creating new categories to exclude and invalidate knowledge that cannot meet this criteria; particularly when the actors developing the metrics involve publishers and states who have historically dominated scientific production. An example of this is the ongoing work of OpenAIRE in promoting its guidelines in national repositories around the world through training workshops to "test the quality of their repositories" and improve it through "guidelines compliance" [START_REF] Bullemore | LA Referencia: Implementing the OpenAIRE guidelines in Latin America[END_REF]. Two cases stand out in regards to their conceptualization of quality: in the Forum Report on Open Data and Open Science in Agriculture held in Kenya, quality is framed in relation to the usefulness and impact of research in its local context, while in the case of Japan's Open Science Policy, quality is framed in relation to how they ensure "fairness" of scientific and technological progress, placing a stronger emphasis on valuebased indicators.

Open Science Research Infrastructures and its Architectural Design 11 Many Open Science policies emphasize the development of research infrastructures and data repositories, particularly for the purposes of advancing open data goals. Japan framed infrastructures as "foundational platforms for sharing results and data" meant to "boost data access and reuse", while the EOSC's ability to offer "secure and seamless access… for storing, managing, and processing data from different sources" was highlighted. In Botswana, a forum report notes the country experiences "similar challenges as other African countries in terms of Open Science", including "utilising and adapting current ICT infrastructure, and building capacity to manage research data." Technical principles and conditions were also referenced to guide the architectural design and function of these research infrastructures, with calls for standards regarding data management, protocols, identifiers, metadata, and more. As part of the OpenAIRE project, the EU advocated for "increased interoperability for more global collaborations, by promoting technological alignment between regions and facilitating the exchange of good practices". An example of this is the Latin American Open Access repository aggregation service, La Referencia, which adopted OpenAIRE Guidelines to "improve global interoperability". OpenAIRE envisions Open Science architecture as "globally networked and distributed", which involves the development of tools such as the OpenAIRE Validator, a tool that "allows repositories to test their compatibility with the OpenAIRE guidelines". [START_REF] Bullemore | LA Referencia: Implementing the OpenAIRE guidelines in Latin America[END_REF] Developing infrastructures also entails developing suitable governance mechanisms: the EOSC was to ensure participating parties develop a "minimal set of rigorously applied and enforced protocols" termed the "Rules of Engagement" to specify conditions which stakeholders could participate. The OECD also aspired for "consultative approaches that involve all relevant actors", while the EOSC noted its intention to be built on "community-based, lightweight, sustainable governance" based on the values of "representativity, proportionality, accountability, inclusiveness, and transparency". However, at the discursive level, it is not clear the extent to which contributions were equitable among stakeholders.

Global Competitive and Collaborative Advantage via Open Science Investment

12 Many countries framed the utility of open science as one of maintaining global competitive advantage, especially given anxieties of "falling behind" in science and technology, and the ability to contribute to the global knowledge base. The "international competitive edge", of Finland's open science and research policies was highlighted, with Europe overall framed as taking a "global leading role", with Open Science enabling them to "remain at the forefront". Simultaneously however, they expressed concern over losing this competitive advantage to countries like Japan, US, and China, given lack of High Performance Computing Infrastructure capabilities (Connected Digital Single Market Policy) which may risk outflow of innovation (EOSC) and dependency on external country facilities. Japan expressed similar sentiments, seeking to "retain its authority as a communicator on the international stage", while expressing anxieties of Japanese researchers becoming less visible and less able to participate in global research, thereby resulting in less international competitiveness given decreased awareness of their science and technology. The African Open Science Platform echoed these concerns, framing participation as benefiting local visibility and contribution to the global knowledge base. That being said, the rhetoric implied Africa was behind, referring to Open Science as a way to "remain relevant in terms of science", noting it needed to "align its scientific activities with... the rest of the world." While competition played a prominent role, aspirations for global collaboration continued with the EOSC policies calling for a "sustainable 'research data commons' ", to reinforce "Open to the World" policies; and talks of a "globally networked... OS infrastructure which would enable a truly 'global and interoperable scholarly commons' ". Meanwhile, EU aims to "improve linkages with neighbouring international cooperation partners outside EU... to facilitate the international dimension" of the Horizon 2020 program, while the G7 similarly advocated for infrastructures enabling data use for all users at a global scale.

Growing role of private sector in Open Science

13 A significant number of policies expressed the need for increased funding and investment in research, with the Mallorca Declaration noting this was essential to bring back funding success rates, so research can attract and maintain funding for their work. Meanwhile, the African Open Science platform suggested Open Science practises could "inform and attract potential funders" and "increase the return on investment" for their research. Most of the policies reviewed indicate that the main source of funding for Open Science initiatives has been thus far, primarily public. Mexico, Japan, and Finland for example outline various guidelines and conditionalities, such as open access publishing as stipulations for publicly funded research. However, public-private financing is increasing in the EU, suggesting that private stakeholders will be key players in the execution and framing of Open Science moving forward, perhaps even necessary as resource constraints may limit the potential of OS initiatives. The EU argued that "the involvement of industry (including SMEs) in the development of research infrastructures" could make them more aware of the opportunities offered by them, and "foster innovative potential" to improve these products. Publishers like STM and Elsevier have already highlighted their alignment, promoting their support to make data FAIR (STM), expressing their commitment to data sharing and more effective data use (Elsevier). The growing role of the private sector is also meant to bolster the development of technologies, which will help drive scientific progress and even societal revolution. STM for example notes the "use of technology to make science more collaborative", while Elsevier emphasises various tools to increase efficiency of search and diffusion. The G7 communiqué also suggests that the convergence of different technologies can be characterised as the "next production revolution", and will "transform production and society as a whole", suggesting a possible reliance on product and service developers.

Core Beneficiaries of Open Science 14 The academic community was unsurprisingly, the most commonly referenced beneficiary, out of all the stakeholders in Open Science. This community includes researchers, scientists, students, academics, and their associated academic institutionslibraries, academies, research organisations, etc. Business and industry also featured prominently, often in the context of innovation. The "Connected Digital Single Market" strategy for example, suggested the deployment of the European Cloud "will improve the capacity of businesses, especially SMEs and the non-tech industry, to innovate and create higher value digitised products". There were also notable references to collaboration between researchers and industry, to support "social innovation and productivity"; and for researchers to promote the advancement of knowledge, to allow "people and firms from all sectors to take full advantage of the benefits of innovation" (EOSC). The emphasis of research infrastructures also saw significant reference to the need for data scientists and technical experts, to develop competencies in open data skills, data management skills, use of infrastructure, analytics technologies, and more (Mallorca, EU Society Work Programme, FAO Kenya Report, Japan). Lastly, Open Science was commonly framed as benefiting citizens due to enhanced accessibility of research, particularly those that are publicly funded (Japan); enhanced accessibility and participation in research via more inclusive (AOSP) and diverse opportunities like "citizen science" projects (G7, OECD), for the ultimate goal of "tightening bonds between science and society" (Colciencias), improve accuracy and public trust in science overall.

Challenges of involving non-experts

15 Despite these ambitions, training citizens with the necessary skills has been noted to be a significant challenge. Colombia, Chile and the UK Royal Society noted that though the "citizen" is framed as someone essential for processes of citizen science, "their effective participation constitutes a challenge for shaping open agendas of research". The EU Work Programme similarly admitted "the formal integration of skills... by non academic actors and provided in non academic surroundings... will be a specific challenge"; while Japan agreed that "the increasing complexity and specialization of science and technology has made it difficult to attract understanding and support from society as a whole". This increasing complexity and specialisation in science and technology is not only an access challenge for citizens, but even for academia, where a "clash of cultures" was noted between data science and academic communities, emphasising the need for core data experts to translate opportunities. (ESOC High Level Expert Group) While participation is a laudable goal, the resources and skills required to engage with these spaces raise the question of how meaningful citizen participation can really be.

Open Science as a Driver of Innovation and Socioeconomic Growth 16 Open Science was also commonly framed as a facilitator and driver of socio-economic growth, particularly to build comparative advantages amidst global competition. This was explicitly stated by the EU who noted Open Science could be "embedded in society as a socio-economic driver", "instrumental in making science more responsive to societal and economic expectations"). G7 and Colombia also both explicitly referred to economic development and socioeconomic growth (respectively) as goals of, and outcomes from their Open Science vision. This is also evidenced by the market-based language of optimising, improving the efficiency (Japan, Elsevier, EOSC, Chile), reducing costs, and ensuring a "return on investment" (African OS Platform) for research, data (Chile), storage (EOSC), search, and diffusion (Elsevier) The "innovation based economy" was also noted as one of the express goals of the Mallorca Declaration, with EOSC noting that the "transition to open science [was to be seen] in the context of the Digital Single Market" strategy. The potential for Open Science to facilitate and drive innovation was also a common theme, particularly through "knowledge exploitation" (Japan) data-driven innovation (LIBER), and the "innovative potential of small-medium-enterprises" (Work Programme). Higher education institutions, such as Aalto University also appear to align, noting its commitment to open science and research goals to ensure research results "can be used as business assets to achieve benefits for society as a whole."

Open Science as a response to global development challenges 17 A few policies framed Open Science as instrumental to addressing global challenges and barriers, emphasizing the value of producing locally relevant knowledge, particularly for developing countries. Colombia claims that one of the benefits of Open Science is "the rapid and joint response to the global challenges of our time… [which] enables the participation of scientists and those interested both in local and global environments in the search for solutions to problems like those identified in the Sustainable Development Goals". The Kenyan Forum Report on Open Science in Agriculture proposes revising the set of metrics used to measure quality and create suitable metrics to track and monitor the visibility of how Open Science facilitates knowledge production, as well as rewards for researchers who use scientific knowledge to solve local problems. The OECD also cites the importance of "international collaboration" within Open Science in the interest of addressing global issues. This multilateral coordination and co-operation was also cited as having a critical role in Research & Development programmes, particularly in the emerging economies of the BRIC (Brazil, Russia, India, and China) countries. We find that policies also envision Open Science as a means to address "global challenges and barriers" for knowledge economies and research institutions; issues pertaining to society at large, such as the SDGs, are fairly broad-brushed.

Breaking dependency on "publish or perish" model 18 Finally, Open Science is also being framed as a paradigm meant to cut the cycles of dependency to the current subscription-based journal system. The European Universities' Associations Statement on Open Science to EU institutions and National Governments recognises the need to change the current publishing system, calling for a more "transparent, cost-effective, and innovative system" that better serves the interests of the research community and research institutions. Key outcomes from the Forum Report on Open Science in Agriculture in Kenya also point to reforms in the current knowledge production system; they claim that the current rewards systems privileges job promotion and visibility of research within high-impact journals, and is not conducive to the Open Science paradigm. They point to alternative forms of incentives, such as rewarding data sharing, as opposed to just rewarding conferences and journals. Despite these calls for reform, the OpenAIRE regional policy cites that tension may nevertheless exist within the competitive "publish or perish" paradigm, even if new collaborative models are in placesuggesting these paradigms will co-exist. In contrast, the EOSC Declaration, takes an economic perspective regarding the value of Open Science, which lies in its potential to reduce rent-seeking behaviour and prepare the market for innovative research services and business models.

Discussion: Situating Open Science Policies in Power Imbalances

19 These key policy narratives do not emerge in a vacuum. Under the premise that openness needs to be "situated" within the histories, social contexts and power structures that shape and constrain them (Hillyer, 2017), one of the main objectives of this research was to contextualize the narratives that are emerging in policies from different parts of the world. One of these contexts is the historical global inequality in scientific knowledge production and exchange systems. A growing body of literature has shown that the current academic knowledge system is unjust and uneven, as it centers and prioritizes knowledge produced by Anglo-speaking male researchers in North America and Europe and shared through international academic journals [START_REF] Graham | Towards a Study of Information Geographies: (im)mutable Augmentations and a Mapping of the Geographies of Information: Towards a Study of Information Geographies[END_REF][START_REF] Larivière | Global Gender Disparities in Science[END_REF][START_REF] Chan | Towards Open and Equitable Access to Research and Knowledge for Development[END_REF][START_REF] Okune | Tackling Inequities in Global Scientific Power Structures[END_REF]Czerniewicz, 2015;[START_REF] Canagarajah | A geopolitics of academic writing[END_REF]. This system has been built on a set of material and symbolic barriers to participation and representation, that range from a widespread lack of internet connectivity and weak digital infrastructures in developing countries, to high subscription costs that impede access to and publication in peer-reviewed journals. This system affects researchers from historically marginalized geographies and identities the most, who in the words of Leslie Chan, remain "nearinvisible and under-valued" (2011), as well as contribute to epistemicide, harming the general public's ability to acquire a more holistic understanding of the world and the issues affecting underrepresented populations (Moletsane, 2014, Hillyer, 2017;Sillitoe, 2007, Hall and[START_REF] Hall | Decolonization of Knowledge, Epistemicide, Participatory Research and Higher Education[END_REF]. Open Science policies have been framed as a change to the aforementioned system, however our analysis of the data suggests that these power imbalances are rather being sustained by epistemic governance mechanisms, in three main ways:

Political pressure and the risk of non-alignment 20 Evidence of epistemic governance can be seen explicitly when member-states and other potential partner-nations of supranational governments, such as the European Union, multinational organizations like the OECD, or initiatives such as OpenAIRE are called upon to align with a specific set of Open Science visions and goals. This alignment is not only a product of jurisdictional commitments, but is often voluntary, as non-state actors such as publishers and university institutions, drive with these visions in order to obtain access to funding and resources. In the case of these actors, the risks of non-alignment are much larger than the loss of autonomy in the process of framing Open Science in a more contextualized manner. These risks include: loss of access to potential funding, global partnerships and membership, and an overall positive reputation in the international community. Investment in Open Science is perceived as a means to prevent innovation outflow and economic decline, and obtain the necessary competencies to remain internationally competitive. Even relatively well-resourced actors such as Japan and member states of the European Union express anxieties about being "left behind" in the growing "knowledge economies." This is especially challenging for regions with resource constraints, already facing pressure to meet foreign scientific standards to stay relevant in the international science ecosystem, while foregoing potential benefits and investment into more locally relevant research to appeal to international interests (Czerniewicz, 2015).

Power imbalances embedded in infrastructure

21 Open Science infrastructure is the second site of epistemic governance. These research infrastructures ubiquitously referenced as "foundational" to the implementation and execution of Open Science policies embed norms, values and dominant scientific practises within its increasingly complex and aspirationally global architecture. Currently, the funders and contributors of these research infrastructures promoted by these policies appear to be concentrated in wealthier regions of the world, raising concerns for how they will amplify the visibility of knowledge produced in these regions, and how this will affect the visibility of under-represented scholarship of the rest of the world [START_REF] Graham | Towards a Study of Information Geographies: (im)mutable Augmentations and a Mapping of the Geographies of Information: Towards a Study of Information Geographies[END_REF]. Their power is intrinsic in the ability to design the architecture of these systems, and ultimately regulate and control how information is produced, accessed, and shared. Once these architectures scale, they become increasingly complex and nearly impossible to change, with the foundational rules setting a path dependency for all other architecture built on top (Lessig, 2006;Sterne, 2012). While the policies reflects intentions of instituting collaboratively defined standards and distributed forms of technical control; it is important to critically consider who is at the table. Other media industries have found standard setting processes often impeded not by technical complexity, but unwillingness of major corporate players to cooperate, thereby-stifling-innovation and interoperability [START_REF] Brooke | Descriptive Metadata In The Music Industry: Why It Is Broken And How To Fix It[END_REF]. Major players in this ecosystem include former publisheroligopolies like Elsevier, rebranded as full-fledged "data analytics" companies via infrastructural acquisitions across the entire research production value chain. (Posada and Chen, 2017). Some have questioned the participation of those with fiduciary responsibility in policy-making processes whose interests are questionable given fiduciary responsibility and accountability to shareholders versus their interest to guarantee public access to knowledge.

Open Science as the road to modernity

The third site of epistemic governance is epistemologies. The emphasis on building knowledge infrastructures in Open Science also risks recentering and codifying European values in the architecture that shapes global science. We have inherited a system that already favors knowledge that is codifiable, useful and "instrumental" over knowledge(s) that cannot be easily codified and transmitted like community-based knowledge, knowledge of lived experience, or spiritual knowledge (Moletsane, 2014;Mignolo, 2012, Hall and[START_REF] Hall | Decolonization of Knowledge, Epistemicide, Participatory Research and Higher Education[END_REF]. Decisions over what infrastructures need to be in place in order to produce Open Science, will shape what knowledges are considered legible, legitimate, worthy of visibility and eligible for quality control (Czerniewicz, 2015[START_REF] Graham | Towards a Study of Information Geographies: (im)mutable Augmentations and a Mapping of the Geographies of Information: Towards a Study of Information Geographies[END_REF]De Sousa Santos, 2007;Windle, 2017). This immediately cedes power to institutions prepared to produce knowledge in these formats, researchers that investigate and communicate science in dominant languages such as English and Spanish, and even to citizens that align with notions of "expertise" in their citizen science practicecontributing further to epistemicide and reaffirming the knowledge paradigms used to justify social, political and economic "leadership" [START_REF] Harding | Must the Advance of Science Advance Global Inequality?[END_REF][START_REF] Moore | A genealogy of open access: negotiations between openness and access to research/ Une généalogie de l'open access: négociations entre l'ouverture et l'accès à la recherche[END_REF][START_REF] Lawson | The politics of open access: Chapter 1[END_REF][START_REF] Mboa | he (Unconscious?) Neocolonial Face of Open Access: Dynamics of Power in Francophone African Universities[END_REF]. This emphasis on infrastructure also suggests Open Science discourse is adopting techno-utopian ideals, reminiscent of the modernisation paradigms that posit a singular universal road to development, forcing developing countries to adapt to modernity via the transfer of knowledge, technology, and services by higher-income countries [START_REF] Albornoz | The Rise of Big Publishers in Development and What Is at Stake[END_REF][START_REF] Grosfoguel | Developmentalism, Modernity, and Dependency Theory in Latin America[END_REF], and amplifying their dependency on concepts, paradigms and infrastructures produced in sites of power.

Conclusion and Future Research Questions

Building on the idea that narratives need to be contextualized and situated within particular histories, power structures and socio-political contexts, we sought to interrogate the assumption that open science is neutral, and investigate how the concepts used to define it at the policy level are shaped by those with institutional power and greater access to resources. For that reason, our analysis seeked to tap onto the ideological foundations at the core of shared narratives of openness across policies, and the impact this may have on reinforcing global inequality in the scientific and development fields.

As seen in the discussion, Open Science frames the rhetoric of openness as a vehicle to promote the need for technological change as part of an inevitable and necessary cultural shift in scientific production. While we are cognizant that infrastructures, protocols and tools are instrumental to producing and sharing science, we are pointing out the ways in which their development is mainly informed by global governance dictums and economic competition agendas, rather than by the demands and unique characteristics of the wide diversity of scientific knowledge producers and users that will eventually rely on them.

The global reach of these narratives, and the technologies, standards and models these narratives sustain, will dictate modes of working and collaborating among those actors who can access and meet them, they also create new categories to exclude and invalidate knowledge that cannot meet this criteria, putting historically marginalized researchers and publics at further disadvantage. For this reason, above all, this study seeks to promote critical thinking about technological change about who it benefits and who may be left behind by the emergence of new trends. We hope these insights contribute to scholarship and activism that promotes more inclusive, community-grounded definitions of Open Science framed around the need for equitable knowledge ecosystems in service of a science that attends to social needs.

25 This research also meant to offer a preliminary overview of topics to unpack further in future research. Our findings first point at the need to follow more closely the funding mechanisms and the involved stakeholders behind the policy making process. Second, it lays the foundations for a more in depth investigation on Open Science infrastructure: the configuration of the architecture, rules of engagement, and the stakeholder experience with these platforms. And third, even though we hinted as a possible disconnect between technical solutions to Open Science and the skills and resources needed to use them, we consider a follow up research on the implementation of these practices necessary to allow us to better gauge the extent to which these narratives were built on the basis of political pressure, public demand or both. As Open Science advocates, we are interested in seeing these initiatives succeed, yet we need to stay vigilant and constantly question their ability to bring about real social and cultural change in knowledge production. NOTES 1. Our search for policies took place from October 2017-March 2018. While we attempted to be holistic in our search, we can't necessarily guarantee that this was an exhaustive and comprehensive list.

2.

We initially intended to focus on Open Science policies only, but found that Open Science is a concept that has been incorporated into Open Access, Open Data, Open Innovation and Open Government policies and programs. Restricting our analysis to policies that explicitly used the term "Open Science" was a methodological decision that limited our research in terms of understanding of programs and policies that practice open science, but have not adopted the same language.

3. The data was collected in this document. The data collection and analysis process was led entirely by volunteers who devoted their resources to this project. The experience of working with volunteers, brought to light some of the limitations of open citizen science, such as limited time commitment, expertise and resources, as well as the benefits of peer-to-peer learning and the enriching experience of collaborative knowledge production.

4.

A list of stakeholders and their associated policies can be found in the Index.

5.

This could be influenced by the language limitations; our team searched for documents only in English, Spanish, and French

ABSTRACTS

Given that "Open Science" is becoming a popular policy object around the world, this study sought to identify key narratives about Open Science in policy, and critically examine the extent to which they are sustaining or strengthening multi-layered domination and inequality schemes that pre-exist in scientific knowledge production. To do so, we conducted a content analysis of Open Science policies stemming from Europe, North America, Latin America, Asia and Africa to understand which narratives about Open Science policies are produced, reproduced and by whom; and in turn, whose interests may be neglected in this process. We found that Open Science policies, mostly stemming from Europe, frame "openness" as a vehicle to promote technological change as part of an inevitable and necessary cultural shift to modernity in scientific production.

The global reach of these narratives, and the technologies, standards and models these narratives sustain, are dictating modes of working and collaborating among those who can access them, and creating new categories of exclusion that invalidate knowledge that cannot meet this criteria, putting historically marginalized researchers and publics at further disadvantage.

INDEX

Keywords: Open science, research infrastructure, epistemic governance, framing theory, policy analysis

AUTHORS

DENISSE ALBORNOZ

The Knowledge G.A.P., Peru albornoz.denisse@gmail.com

MAGGIE HUANG

The Knowledge G.A.P., University of Toronto Scarborough, Canada maggie@cis-india.org

ISSRA MARIE MARTIN

The Knowledge G.A.P., University of Toronto Scarborough, Canada im.martin@mail.utoronto.ca (corresponding author)

MARIA MATEUS

The Knowledge G.A.P., University of Toronto Scarborough, Canada mateus.maria0@gmail.com (corresponding author)

AICHA YASMINE TOURÉ

The Knowledge G.A.P., University of Toronto Scarborough, Canada aichay.toure@mail.utoronto.ca (corresponding author)

LESLIE CHAN

The Knowledge G.A.P., University of Toronto Scarborough, Canada chan@utsc.utoronto.ca (corresponding author)

Framing Power: Tracing Key Discourses in Open Science Policies ELPUB 2018

Framing

  Power: Tracing Key Discourses in Open Science Policies ELPUB 2018

Framing

  Power: Tracing Key Discourses in Open Science Policies ELPUB 2018

Framing

  Power: Tracing Key Discourses in Open Science Policies ELPUB 2018

Framing

  Power: Tracing Key Discourses in Open Science Policies ELPUB 2018

  BIBLIOGRAPHY Sterne, Jonathan. (2012). MP3: The Meaning of a Format. Sign, Storage, Transmission. Durham: Duke University Press. Windle, Joe. (2017). "Hidden Features in Global Knowledge Production: (re)positioning Theory and Practice in Academic Writing." Revista Brasileira de Linguística Aplicada 17, no. 2: 355-78.

Continent/Country

G7 International

The Group of Seven (G7) is an informal bloc of industrialised democracies. Its current members make up nearly 50 percent of the world economy, and represent more than 60 percent of net global wealth.

Multilateral Organisation G7 Science Communiqué OECD International

The

Global Young Academy International

The Global Young Academy aims to become the voice of young scientists around the world. To realise our vision, we develop, connect, and mobilise new talent from six continents. Moreover, we empower young researchers to lead international, interdisciplinary, and inter-generational dialogue with the goal to make global decision making evidencebased and inclusive.

Scientific Organisation (Non-State

Actor)

GYA Academy Statement on Open Science

International Council for Science (ICSU) International

The International Council for Science is an international organization devoted to international cooperation in the advancement of science. Its members are national scientific bodies and international scientific unions. is specialized agency of the United Nations that leads international efforts to defeat hunger.

Our goal is to achieve food security for all and make sure that people have regular access to enough highquality food to lead active, healthy lives. With over 194 member states, FAO works in over 130 countries worldwide. We believe that everyone can play a part in ending hunger.

Sci-GaiA

Africa/ Senegal

The Sci-GaIA project-"Energising Scientific Endeavour through Science Gateways and e-Infrastructures in Africa"-is an EU Horizon 2020 funded project, under the grant agreement no: 654237. The project started on the 1st of May 2015 and will last for 24 months. Sci-GaiA aims at creating a sustainable foundation of educational materials and procedures for the development and management of Science Gateways and e-Infrastructures in Africa and beyond.

EU Funded


Yu-Shen Yang

Andreas Veneris

Paul Thadikaran

Srikanth Venkataraman

Extraction Error Modeling and Automated Model Debugging in High-Performance Low Power Custom Designs

Test model generation is common in the design cycle of custom made high performance low power designs targeted for high volume production. Logic extraction is a key step in test model generation to produce a logic level netlist from the transistor level representation. This is a semi-automated process which is error prone. This paper analyzes typical extraction errors applicable to clocking schemes seen in high-performance designs today. An automated debugging solution for these errors in designs with no state equivalence information is also presented. A suite of experiments on circuits with similar architecture to that found in the industry confirm the fitness and practicality of the solution.

Introduction

Large and complex VLSI designs such as microprocessors, SOCs and ASICs often require high-performance low power custom logic blocks designed at the transistor level [START_REF] Ando | A 1.3-ghz fifth-generation sparc64 micrprocessor[END_REF][3] [START_REF] Kurd | A multigigahert clocking scheme for the pentium R 4 microprocessor[END_REF]. Since a transistor level representation cannot be used directly to generate production tests, a gate level (logic) representation of these blocks, also known as a test model, is extracted from the transistor schematic and used instead. A rough outline of the steps in test model generation is found in Figure 1 [6] [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF]. The test model is generated according to the functionality of the extracted netlist, additional synthesized logic and external test vector constraints using logic extraction [START_REF] Kundu | GateMaker: A Transistor to Gate Level Model Extraction for Simulation, Automatic Test Pattern Generation and Verification[END_REF]. Logic extraction is a semi-automated process which is error prone [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF]. Since the amount of verification on the test model before test generation may be limited, extraction errors may carry forward and discovered during test validation.

As soon as the test model fails verification/validation, manual debugging is performed. This is a time-consuming and resource intensive process that may delay test delivery to the factories and/or reduce their quality. Therefore, automated error extraction debugging tools benefit the test model generation cycle. Knowledge of these error types is also useful because it can be used to annotate and improve the extraction process. It should be noted, the nature and the effects of extraction errors are radically different to the ones by common design errors presented in [START_REF] Abadir | Logic verification via test generation[END_REF]. Therefore, existing design error debugging techniques [START_REF] Veneris | Design error diagnosis and correction via test vector simulation[END_REF] may not be applicable/efficient when applied to extraction errors.

The work of Yang et al. [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF] presents a set of common extraction error types. However, that methodology [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF] is limited in the sense that it handles test models that have a one-to-one mapping of their state elements to those of the transistor level representation. It also does not examines extraction errors that may occur in the complex 1 University of Toronto, Department of Electrical and Computer Engineering, Toronto, ON M5S 3G4 ({yangy, veneris }@eecg.toronto.edu) 2 University of Toronto, Department of Computer Science, Toronto, ON M5S 3G4 3 Intel Corporation, Architecture Group, Hillsboro, OR 97124 ({paul.thadikaran, srikanth.venkataraman}@intel.com) clocking scheme of high-performance designs. In this paper, we add and complement to this work [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF] as follows:

• We examine common error types found in the clocking scheme of the test model and by proposing a debugging in contemporary microprocessor, ASIC and SoCs blocks. A suite of experiments on designs with similar architecture to that found in industry demonstrates the practicality of the approach. Methods such as the one presented here benefit the testing of microprocessors, ASICs and SoCs. They help in identifying model inaccuracies at early stages of the design cycle to reduce test delivery turnaround time and improve test model generation.

The paper is structured as follows. Section 2 contains preliminaries and it reviews prior work. Section 3 reports types of extraction errors in the clocking scheme of modern designs and Section 4 presents the automated debugging methodology. Section 5 contains experiments and the last section concludes the paper.

Background

Extraction builds a logic representation from a custom transistor level block [START_REF] Kundu | GateMaker: A Transistor to Gate Level Model Extraction for Simulation, Automatic Test Pattern Generation and Verification[END_REF]. This representation is later used for test generation. This work assumes that the extracted netlist contains (N)AND, (N)OR, NOT, tri-state buffer and flip flop primitives. For these primitives, we define the controlling value of a (N)AND ((N)OR) to be a logic 0(1). In implementation, we use a zero delay simulator for all combinational circuitry.

All designs used here are two-stage strictly pipelined to resemble the architecture of custom high-performance cores found in modern designs. This architecture is shown in Figure 2 where combinational logic A, B and C is completely separated by layers of register files I and II with memory elements. We refer to these parts of the design as the core circuitry. Since we assume that there is no feedback in the circuit, all test vectors used in experiments are two clock cycle input test patterns.

The clocking circuitry of the design is also shown in that figure [START_REF] Ando | A 1.3-ghz fifth-generation sparc64 micrprocessor[END_REF][3] [START_REF] Kurd | A multigigahert clocking scheme for the pentium R 4 microprocessor[END_REF]. The difficulty in implementing designs operating at high frequencies is that not only do high frequencies cause high clock skew and jitter, but they also increase the power consumption.

Figure 1. Test Generation Flow

There are several sources of extraction errors during test model generation [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF]. Logic extraction may introduce errors in the final flattened netlist due to erroneous module mappings. An additional source are functional mismatches in the definition of module operation constraints between different libraries (simulation library, synthesis library, physical design library, ATPG library, etc). In this case, the mapping is correct but the operation of the module is interpreted differently by various libraries. In both cases, these errors may change the functionality of the extracted test model.

It is important to note that a single error contained in a module definition may "translate" to multiple erroneous instantiations in the extracted netlist [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF]. This library module mapping is known to the engineer who can use it to debug the design. However, since automated extraction tools are usually involved, a one-to-one correspondence between all register files of the transistor-level and gatelevel representation may be lost [START_REF] Kundu | GateMaker: A Transistor to Gate Level Model Extraction for Simulation, Automatic Test Pattern Generation and Verification[END_REF] [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF]. In other words, state equivalence information between the transistor level and the test model may not be fully available to the engineer. Obviously, complete or partial lack of knowledge of this state equivalence information adds to the complexity of the overall debugging problem. The work of Yang et al. [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF] reports types of errors at the core of the circuit (test model) that are typical during extraction. Since the debugging approach in Section 4 handles all types of errors reported in [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF], and to illustrate the nature of the presented problem, we briefly review these core error types.

Outputs

Roughly speaking, core circuit errors are divided into three categories. The first category contains errors that may happen on a flip flop. For example, an asynchronous memory element may be mapped erroneously to a synchronous one and vice versa.

D Q Q D Q D Local Global Clock1 Global Enable Loc Clk1 Loc Clk2 Loc Clk3 Local Enable Clock2 Local Global Clock1 Global Clock2 Enable Clock2 Global Global Clock1 Clock3 Glb Clk1 Glb Clk2 Clock1 Enable Clock2 (d)

Figure 4. Gated Clock Error

Library specification mismatches on the priority of clock and reset line contests may also cause the test model to behave erroneously when compared to its transistor level implementation.

The other two categories include errors that relate to different multiplexer and multiplexer-latch implementations shown in Figure 3. In that figure, a triangle with no circle indicates a tristate buffer. It can be shown, with the use of appropriate truth tables [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF], depending on the input values of each module, the output may be different. In practice, an extraction error arises from any replacement of one module for another. For example, one module implementation from the three implementation types shown in Figure 3(a) maybe mistakenly implemented with another module from Figure 3(a), etc. The net result is that this module misplacement may produce an erroneous test model that misleads test generation.

Error Types in Clocking Circuitry

In this section we present a set of extraction error types that are common to the clocking circuitry of high-performance cores for microprocessors, SoCs, ASICs, etc in the industry today [START_REF] Ando | A 1.3-ghz fifth-generation sparc64 micrprocessor[END_REF][3][5].

Gated Clock

Modern devices impose strict power consumption and reliability requirements. Since not all design components may always need to operate simultaneously, the gated clock scheme of Figure 4

Local Clock Frequency Divider

Frequency dividers are used on approximate domain global clocks to generate integral local clock frequencies that drive various design blocks at different speed [START_REF] Bearden | A 780mhz powerpc microprocessor with integrated l2 cache[END_REF] [START_REF] Kurd | A multigigahert clocking scheme for the pentium R 4 microprocessor[END_REF]. Figures 5(a) and 5(b) contain common hardware to implement such dividers. If the extraction process erroneously introduces a NOT gate at the input of the Enable line, the new local clock generated will be enabled at complementary phase (Figure 5(c)). This may result in a circuit malfunction because the memory elements of the core will lock/propagate different logic values.

Local Clock Pulsed Buffers

Local clock buffers are used to generate clocks of a desired frequency for various design blocks [START_REF] Bearden | A 780mhz powerpc microprocessor with integrated l2 cache[END_REF] [START_REF] Kurd | A multigigahert clocking scheme for the pentium R 4 microprocessor[END_REF]. These buffers are driven from global clocks through delay-matched taps. Different local clocks drive the various critical and non-critical units of the microprocessor in appropriate frequencies that guarantee performance yet save power. These buffers are available as pulsed and nonpulsed. Non-pulsed drivers simply buffer the input global clock and they usually present no problem to the extraction process.

Figure 6(a) shows a medium pulsed clock driver. At the rise of the global clock, the pull-down path is asserted to generate the rising edge of the local clock. At the same time, the self-reset pullup path is asserted to generate the falling edge of the clock. The delay buffer is adjustable to permit different types of duty cycle for the output local clock. Variations of the hardware in Figure 6(a), shown as a box in Figure 6(c), allow for pulsed buffers that generate slow and fast frequency local clocks from global clocks. We omit these hardware descriptions that can be found in [START_REF] Kurd | A multigigahert clocking scheme for the pentium R 4 microprocessor[END_REF]. Additionally, Figure 6(b) shows the schematic for another medium pulsed clock driver with complementary phase to the one of Figure 6(a).

During extraction, erroneous mapping or mismatches in library specifications may utilize a different pulsed buffer in place of the other. For example, a medium frequency buffer may be accidentally replaced with a slow frequency one or with one with inverted phase. From real life experience, it is usually unlikely that a fast frequency buffer will be replaced by a slower one, although the debugging method, presented next, can handle this case. When a pulsed buffer replacement error occurs, it may change the functionality of the test model and the input/output vectors collected in test generation may give faulty output responses during vector validation.

Debugging Pipelined Designs

In this section we present an automated simulation-based debugging method for single extraction errors. Automated debugging involves two steps namely diagnosis and correction. Diagnosis identifies lines in the circuit where error effects may originate and correction proposes a module modification (replacement) on these lines to rectify the design. These modifications are selected from the types of extraction errors presented in the previous two sections. Since module mapping information is known to the engineer, it suffices for diagnosis to identify a single line driven by an error module. All other lines with similar erroneous modules are discovered by using the mapping information.

One basic ingredient of diagnosis is a enhanced path-trace procedure. Path-trace is a line marking algorithm originally proposed by Venkataraman and Fuchs [START_REF] Venkataraman | A deductive technique for diagnosis of bridging faults[END_REF] to aid fault diagnosis. In the current work, we modify the procedure to accommodate the needs of the problem. The modified procedure simulates an input vector with failing responses and begins by marking an erroneous primary output. Subsequently, if the output of a gate is marked and all its inputs have non-controlling values, path-trace marks all inputs of the gate. If the output of a gate is marked and its inputs have one or more controlling values, path-trace randomly marks one such input. If the output of a gate is marked and its input have only logic unknown (X) and/or non-controlling values, path-trace marks all input that has an X. Finally, if the gate is a memory element, its input, reset and clock lines are always marked.

Due to the pessimistic nature of path-trace, it can be shown that it always marks at least one line driven by an error module. We omit the proof of this claim which is similar to the one found in [START_REF] Liu | Incremental diagnosis of multiple open-interconnects[END_REF] that contains another 3-valued variation of path-trace.

Additional care needs to be taken once path-trace marks the output of some memory element of the pipeline. Although the procedure correctly marks (among other lines) the input of the flip-flop, the logic values in the circuitry of the fan-in cone of this input may be obsolete. This is because of the different clock domains that trigger independently and may change the values of the lines in the fan-in cone that feed the marked flip-flop.

To elaborate further, consider the architecture in Figure 2 and assume that the memory elements in register files I and II are triggered by two independent clock domains as shown in Figure 7. Furthermore, assume that flip-flop F in file II driven by clock1 is marked by path-trace at time-frame t7. In other words, path-trace started from an erroneous output marking lines in combinational circuitry C as it reaches the output of F .

The reader may verify that any error effect that propagates to F , it did so using logic values in combinational circuitry B that corresponds to time frame t5 (or before), that is, prior to the trigger of clock1. This is because local clocks (including clock1) may be shared with register file I and overwrite the values in circuitry B past time frame t5 when clock1 triggers to store values in F. Since these values are now lost, we need to devise a way to restore them so that path-trace continues marking lines at the input of F correctly. Equivalently, if logic values in circuitry B are updated correctly and a memory element in register file I is marked, we need to restore all values of the gates in combinational logic A at time frame t3 to have path-trace continue marking lines correctly.

The above discussion indicates that one needs to store the logic values of each logic primitive of the core for all time frames as different clocks trigger. These stored values can be recovered to guide path-trace to mark suitable sets of lines at appropriate time-frames. In the proposed implementation, these logic values are stored in the file registry and the primary input of the circuit. This is done with respective storage tables updated appropriately throughout the simulation of the circuit. Once an erroneous primary output is observed, the stored table values are simulated in the combinational block they fan out to restore the logic values on the primitives at correct time frames. We illustrate the need and utilization of the storage tables with an example.

Figure 7. Example Clock Waveform

Example: Consider the circuit in Figure 8(a) with Register Files RF I = {F1, F2, F3) and RF II = {F4, F5} and combinational core A, B and C that contains a single error with a single instantiation. This is the multiplexer implementation, shown within the dotted box, that needs be replaced with the leftmost (inverted) module from Figure 3(a). Assume the two clocks of the design have the timing characteristics from Figure 7. In the same figure, the aforementioned tables are also shown. Observe, a pair of tables is dedicated to the Primary Input (PI) and a single table to register file RF I. The column marked as C1 (C2) corresponds to information that pertain to Clock 1 (Clock 2). These tables do bookkeeping of logic values throughout simulation and they are updated at every clock trigger as follows.

All entries of P I Table I are initialized to logic unknown X. Assume that at time t0 the first input vector 0001 is applied at the input. This vector excites the error and error values propagate to RF I. At time t1, both clocks trigger and all the tables are updated as shown in Figure 8(a) to reflect logic values of the memory elements and the primary input prior to that clock trigger.

The RF I table is updated first with the original values of the flip flops in RF I. In this case, all memory elements are initialized to X. Since both clocks trigger, all columns of the P I Table II for all f ∈ RF I do for all i ∈ P I do 16:

are X X X X F1 F2 F3 F4 F5 C1 C2 C1 C2 X C1 IN0 IN1 IN2 IN3 C1 C2 0 0 0 1 0 1 0 0 C1 C2 F1 F2 F3 X X X X X X C1 C2 C1 C2 C1 C2 X X X (b) (a) F5 1 1 X F3 F2 F1 C2 C1 F4 F3 F2 F1 0 0 1 1 C2 C1 IN3 IN2 IN1 IN0 (d) PI Table II PI Table I RFI Table 0 0 1 0 (c) 1 PI Table II RFI Table PI Table I IN3 IN2 IN1 IN0 X 0 0 0 IN3 IN2 IN1 X X IN0 C2 X X X X X 0 Q D Q D Q D Q D Q D X Q D Q D Q D Q D Q D Algorithm 1
T P I 1(c, i) ← the value of i end for 34: end procedure column of P I Table II. Once these input values are simulated in block A, path-trace continues to mark the error location.

Algorithm 1 contains pseudo code for the procedure UP-DATE TABLE that updates the storage tables at every clock trigger during simulation. The same figure contains the pseudo code for ENHANCED PATH TRACE that uses these values to assist pathtrace in diagnosis. This is also the central procedure used in diagnosis. The first procedure works as follows. For every clock c ∈ C triggered at time t, the RF I Table is updated first with the current values of the flip-flops in register file I (line 10). Next, the PI Table II is updated by copying PI Table I to the column which is mainindexed with c (line 12). Intuitively, the columns of PI Table II under the same main index contain the values of the primary inputs prior to the last indexed clock triggered. Finally, the PI Table I is updated with the input vector (line 16).

The values stored in the tables are used appropriately by EN-HANCED PATH TRACE to restore logic values in the core circuitry and assist path-trace during diagnosis. When path-trace marks the registers in register file II, it reads the proper entry in RFI Table, and retrieves the correct values in combinational circuitry B by simulating the circuitry with the values read from the table. Then, pathtrace proceeds by marking in the combinational circuitry B (lines 24-26). When path-trace reaches the registers in register file I, it again reads the entry in PI Table II according to clocks controlling the registers on the path and restores the states of the lines in combinational circuitry A. Path-trace continues marking in core circuitry A and terminates when it reaches a primary input of the circuit, as explained earlier. These actions are taken in lines 29-31.

After path-trace returns a set of candidate error locations, the algorithm ranks these locations in terms of path-trace counts and enters correction where error sites are visited in descending order of marks by path-trace. All potential error modifications are exhaustively enumerated on these sites and the circuit is re-simulated for the input test vectors. Corrections that return correct primary output results for the test vectors qualify debugging. For example, once diagnosis returns the error site pointed in Figure 8(a), correction will enumerate candidate correction models from Figures 3(a

Experiments

Tests are carried on two-stage pipelined sequential designs (Figure 2) built with modified ISCAS'85 and ITC'99 combinational circuitry on a Pentium R 2.8 GHz processor. The clocking circuitry of these circuits consists of a scheme of 4 global clocks that drive 21 local clocks. The frequency of the global clock domains is an integral multiple of each other. The circuit names, primitive count and memory element count are found in Table 1.

There is a total of 11 different types of extraction errors presented here and in [START_REF] Yang | Extraction error diagnosis and correction in high-performance designs[END_REF]. To emulate a real physical synthesis environment, we realize 2-3 different module libraries for each error type for a maximum of 33 module types. In practice, this indicates a set of modules with the same functionality but different physical characteristics. For each circuit, we perform three types of experiments using the debugging algorithm from Setion 4 where all information, some (partial) information and no information of state equivalence is known, respectively. In the case of partial state equivalence, we randomly utilize 50% of the state equivalence information.

Each experiment contains averages of 15 runs and times are in seconds. To exhibit the effectiveness of the proposed debugging approach, we compare its performance with a brute-force method where the engineer debugs the design manually by simply enumerating all module libraries. This brute-force approach is the common The next two columns contain the average number of modules examined by manual debugging (brute-force) versus the one marked by path-trace. We observe, the enhanced path-trace has good resolution as it eliminates more than 40% (on the average) of useless module enumerations. The next three columns contain CPU times for the brute-force and the proposed debugging algorithm; Column 4 contains the time for the brute-force method to find all possible solutions, column 5 the respective time for the proposed approach to find one solution and column 6 the time to find all solutions. The solution may not be unique due to fault equivalence [START_REF] Veneris | Design error diagnosis and correction via test vector simulation[END_REF]. As seen from the speed up ratio (last column), the proposed approach reduces the effort dedicated to test model debugging by a factor of 3 when it searches for all solutions. The same experiments (not reported here) also show that the speed up is approximately 11 times when the method exits after the first solution.

Figure 9 plots the run-times of the different cases of state equivalence information availability from Tables 2, 3 and 4 for four circuits. In three cases the CPU saving due to the information provided by the state equivalence is reflected in the final debugging effort. This is expected because state equivalence eases the task of path-trace as it reduces the number of time frames it has to traverse. In fact, this is the case for all circuits in our experimental set up except circuit A (dotted line) where the run time does not monotonically reduce as more state information becomes available. This is because the number of candidates marked by path-trace varies and it has an impact in the overall debugging time.

In the future, we plan to investigate other discrepancies in test model generation for different circuit architecture types with feedback and further refine the debugging approach.

  Figure 2. Benchmark Architecture

  Figure 3. (a) MUX Implementations (b) MUX-Latch Implementations

  (a) is commonly used to disable clocking of temporarily inactive components and save power[START_REF] Ando | A 1.3-ghz fifth-generation sparc64 micrprocessor[END_REF][START_REF] Kurd | A multigigahert clocking scheme for the pentium R 4 microprocessor[END_REF]. The hardware in that figure is built in such a way so that the Local Clock 1 operates in the frequency of Global Clock 2 as long as Enable is at a logic 1.

Figure 5 .

 5 Figure 5. Frequency Divider Error

10 :T

 10 RF (c, f ) ← the state of f

for all r 1 ∈c 1 ←for all r 2 ∈c 2 ←

 1122 20: procedure ENHANCED PATH TRACE(T P I 1, T P I 2, T RF , C, RF I, P I) 21: path-trace on comb. circuitry C 22: RF 2List ← marked registers in RF 2 23: RF 2List do 24: clock of r 1 25: read and place values in T RF (c 1 ) on registers in RF 1 26: simulate and path-trace comb. circuitry B 27: RF 1List ← marked registers in RF 1 28: RF 1List do 29: clock of r 2 30: read and place values in T P I 2(c 1 , c 2 ) on P Is 31:simulate and path-trace on comb. circuitry A

  debugging practice in the industry today when the test model fails since conventional design error techniques do not apply due to the different nature of the errors.Tables 2, 3 and 4 contain results for the all-, no-and partialstate equivalence case, respectively, presented in a similar manner as follows. The first column has the circuit name. Debugging results shown in the remaining columns are based on simulation of 2,000-3,000 erroneous vectors with high fault coverage (> 90%).

Figure 9 .

 9 Figure 9. Run-time vs. State Information

  To maintain performance, minimize power, reduce noise and lower clock skew and jitter, contemporary clock systems distribute clocks at lower frequencies, generate faster local clocks and enable multilevel clock gating [2][3][5]. Therefore, different types of clock manipulator components are used to locally generate clocks with the required frequencies.

	Custom	Synthesized
	Logic	Logic and	Golden
	Level) (Transistor	Constraints Test	Model
	Gate Level Extraction
	Test Model Generation (CUT Netlist)
	Fail
		Test Model Verification
	Fail	Pass
		Test Generation
		Test Validation
		Pass
	Model Debugging	To Tester

Table 4 :

 4 Table Update and Enhanced Path-Trace 1: T P I 1 := PI Table I 2: T P I 2 := PI Table II 3: T RF := RF I C := Clocks triggered at time t 5: RF I := the set of registers in Register File I 6: P I := the set of primary inputs 7: procedure UPDATE TABLE(T P I 1, T P I 2, T RF , C, RF I, P I)

	8:	for all c ∈ C do
	9:

Table 1 . Circuit Characteristics

 1

	) and 3(c)

Table 2 . All State Equivalence Designs

 2

Table 3 . No State Equivalence Designs

 3

	ckt	# of module		time (sec)
		Brute Path-Brute-	Proposed	Speed
		Force Trace Force	One	All	up
	A	30.0	5.9	51.4	3.8	8.9	5.8
	B	30.6	10.8	132.5	26.4	50.1	2.6
	C	32.0	11.9	226.5	60.0	90.5	2.5
	D	30.6	12.3	256.3	62.7	99.9	2.6
	E	31.3	8.3	170.0	25.7	38.7	4.4
	F	31.3	14.3	601.6	65.3	88.6	6.8
	G	32.3	11.7	247.6	29.2	48.1	5.1
	H	31.0	12.8	486.8 120.9 185.0	2.6
	Avg	31.1	11.0				4.1

Table 4 . Partial Equivalence Designs

 4

Proceedings of the Design, Automation and Test in Europe Conference and Exhibition (DATE'05)

Proceedings of the Design, Automation and Test in Europe Conference and Exhibition (DATE'05) 1530-1591/05 $ 20.00 IEEE


Javier Arias

Collecting Inclusive Usage Metrics for Open Access Publications: the HIRMEOS Project

Introduction

Usage metrics have been widely adopted in Open Access works as an indication of the popularity or acceptance of a particular publication [START_REF] Kumar | Research Evaluation Metrics[END_REF]. Inevitably, performance assessment and funding allocation are being based on these statistics [START_REF] Giménez-Toledo | The Evaluation of Scholarly Books as Research Output. Current Developments in Europe[END_REF], making metrics collection and reporting a fundamental need for any organisation producing and/or hosting digital monographs.

A main characteristic of Open Access publications is the ease of redistribution that their licences entitle, allowing dissemination across multiple platforms [START_REF] Suber | Open Access Overview[END_REF]. However, while most distributing platforms produce usage metrics for their own analysis, these are rarely made available publicly via APIs that enabled programmatic collection of such metrics, preventing less technologically advanced organisations from harvesting this data.

Publishers who want to obtain metrics from distributing platforms struggle to find consistency in the book identifiers used, varying from ISBN numbers to URLs, DOIs, or even the publication title, or the platform's own database serials. Distributing platforms also struggle to obtain comparable data from peer organisations and to produce reliable metrics themselves. These intricacies mean a high entry barrier for small publishers and university presses that can neither implement an existing standard due to high validation fees nor can they develop their own metrics software.

In order to help tackling these problems, the HIRMEOS project presents Work Package 6, in which Open Book Publishers is working to create and populate a database of titlespecific usage data-aggregating metrics across multiple different platforms and formats. Drivers are being developed to harvest alternative hosting platforms for usage data, and an API created for publishers and platforms to query the database and present the aggregate data on their own websites. All the code and architecture created for this project will be Open and be made available for others to freely download, adopt or adapt as they wish, facilitating broader uptake, collection, and presentation of this data for Open Access publications.

Design and Methodology

Collecting usage data for books presents a set of challenges different than for articles. Authors and publishers are interested in obtaining overall usage data for both the book and individual chapters within a book. Even citation identifiers such as the DOI remain limited for books. Books and individual chapters have different identifiers. Many platforms hosting digital editions of OA titles assign their own DOIs or permanent URL references to the content, as well as numerous different digital formats for ebooks exist and circulate.

Statistics Collection Agent 6

The Statistics Collection Agent will be developed in a modular fashion, where each of the modules constituting the software will be called drivers; one for each of the disseminating platforms which one wish to collect metrics from. These drivers will work independently of one another, minimising the risk of failure, and allowing future expansions and the implementation of new drivers; as well as publishers will not need to run or even install, drivers for those platforms in which they do not possess any content. The role of these modules will be both the download of metrics from its disseminating platform and the upload to the remote database via the metrics API. Publishers will be encouraged to schedule drivers to run daily via cron jobs in order to keep metrics updated. The list of drivers -subject to future expansion-that will be developed in this project includes Google Analytics, Access Logs, Google Books, Open Edition, OAPEN, Wikimedia, Unglue.it, The Classics Library, OpenAIRE, IRUS-UK, JSTOR, Matomo (Piwik), and World Reader. The Google Analytics and Matomo drivers will be provided as a generic template that will allow disseminating websites (and publishers who also act as such) to develop a driver for their own online readers; those who do not wish to share their usage data with commercial third-parties will be able to use the Access Logs driver to achieve the same purpose. An Access Logs based driver to provide download metrics will also be provided. Some of the disseminating platforms provide APIs that can be queried to obtain metrics, others require web scraping. Even though generic drivers for each use case cannot be provided given that each API or web interface will differ from one another, it is expected that publishers will be able to create new modules for platforms not covered in this project based on existing drivers that follow the same methodology they intend to produce. For instance, Google Books does not provide a metrics API, only a reporting interface where publishers and authors can download a CSV report; taking its driver as an example it would be trivial to develop a new module for a different platform which lacked an API but provided metrics reports. The architecture of the service can be appreciated in greater detail in the figure below.

OA

Metrics

Services Architecture

Open Book Publishers: CC-BY

Identifier Translation Service

Each disseminating platform makes use of a different identifier for their content: Google Books using ISBN numbers, World Reader book titles, Open Edition URLs, etc. In order to produce reliable metrics for a publication, one must first convert the primary keys obtained from each platform to a common identifier.

The Identifier Translation Service consists of a database populated with identifiers for each publication the publisher or platform is interested in, stored in the form of Uniform Resource Identifiers (URI). URIs provide a simple and extensible means for identifying a resource [START_REF] Berners-Lee | Uniform Resource Identifier (URI): Generic Syntax[END_REF], allowing the storage of any possible identifier a book may have, standardised using an Open Standard freely available to all. Thus, an ISBN number such as 978-1-78374-368-1 would be stored using the URN scheme [START_REF] Moats | URN Syntax[END_REF] as urn:isbn:9781783743681; a DOI like 10.11647/obp.0001 using the "info" scheme (Van de [START_REF] Van De Sompel | The 'info' URI Scheme for Information Assets with Identifiers in Public Namespaces[END_REF] as info:doi:10.11647/obp.0001; and custom platform-specific identifiers may be stored using the "tag" scheme [START_REF] Kindberg | The 'tag' URI Scheme[END_REF].

Users may query this API providing either a URI or a title, and indicating the URI scheme and namespace of the identifiers they want to retrieve. Since identifiers are normally shared between the book and its chapters, the user may also specify the type of work they are interested in (e.g. monograph, book-section, book-set, etc.), and indicate whether they want a unique result to be retrieved by setting a strict boolean flag to true, which will trigger an error if multiple results are found in the query. Resolution of fuzzy titles to URIs is also possible using a result scoring system that uses Levenshtein distance to determine the fittest candidate in a search query.

There will be occasions where a single publication has been assigned multiple identifiers of the same type (e.g. multiple DOIs) and the translation service will fail to programmatically return a single mapping. In order to avoid heuristic resolutions, which are very error prompting, publishers will be allowed to assign a canonical URI within each scheme for each publication; thus ensuring a conscious resolution of the conflict.

The translation API will, therefore, enable the storage of book metadata, and facilitate DOI conflict resolution through canonicalisation. Publishers will be able to perform these actions with the provided front end, or they can otherwise integrate them into their existing administration systems.

Measures in the Metrics API

The metrics database and API will enable the storage of results provided by the other services, where metrics will be stored against a URI representing the object of study, following the same specifications as the Translation Service.

Although the list could potentially be extremely extensive, disseminating platforms tend to keep the number of measures offered to a minimum, normally exposing only book views, but sometimes also page views, book downloads, and/or unique book views. One of the ambitions of defining this format is to achieve a sufficient level of flexibility that will allow its application to metrics collected from different platforms that are already using different criteria but also to anticipate to future specifications. Consequently, there cannot exist a predefined list of available measures in this standard, but rather a set of rules that allow defining them.

The concept of a measure is made of three components: a platform, a namespace, and a type; and that each measure will be identified by a unique name within its namespace.

The platform component will identify where the data was collected from (e.g. Google Books, Open Edition, World Reader, etc.), and the type will reflect what the measure represents (e.g., a visit, a download, a session, etc.). These two components alone are actually sufficient to define a measurement, however, they fail to identify those measures that can be aggregated together. Instead of imposing a unique point of view, it is desired to accommodate each different interpretation of measure aggregation allowing the users of this standard to create their own definitions.

Each set of measure definitions will be collected using the "tag" URI scheme, under a particular namespace that indicates the organisation defining such measures (e.g. operas.eu), and will represent a standard of the different measures that can be aggregated together, as agreed by the parties involved in the definition of that particular namespace. Consequently, namespaces represent not only a flag for technical purposes but also an indication of the organisation that takes responsibility for its definition. For instance, partners of the OPERAS consortium may use the "tag:operas.eu,2018:readership" namespace to identify measures related to book readership that they consider to be comparable, differentiating book visits from Google gathered in Google Books using "tag:operas.eu,2018:readership:google-books", from book downloads harvested from the OAPEN library "tag:operas.eu,2018:downloads:oapen".

Although the flexibility of measure definition may not solve the lack of consistency in usage metrics, one of the main aims of this design is to raise awareness that we must not impose the use of metrics standards to organisations that may not have the resources to implement them. However, it is also fundamental that the variety of statistics collected by these services is acknowledged when reporting them to users, and that a transparent and comprehensive broken down view is provided at all times.

Conclusion

16 Very few efforts have been made towards achieving a comprehensive and transparent mechanism to collect and aggregate usage metrics. Most platforms are limited to collecting their own usage, while many do not even comprehend the complexity of the matter. The HIRMEOS project poses a groundbreaking approach that enables metrics collection and aggregation from third-party platforms, which is currently a manual job for many scholarly publishers that lack the funds to find a technical solution to the problem.

BIBLIOGRAPHY

ABSTRACTS

Open Access has matured for journals, but its uptake in the book market is still delayed, despite the fact that books continue to be the leading publishing format for social sciences and humanities. The 30-months EU-funded project HIRMEOS (High Integration of Research Monographs in the European Open Science infrastructure) tackles the main obstacles of the full integration of five important digital platforms supporting open access monographs. The content of participating platforms will be enriched with tools that enable identification, authentication and interoperability (via DOI, ORCID, Fundref), and tools that enrich information and entity extraction (INRIA (N)ERD), the ability to annotate monographs (Hypothes.is), and gather usage and alternative metric data. This paper focuses on the development and implementation of Open Source Metrics Services that enable the collection of OA Metrics and Altmetrics from third-party platforms, and how the architecture of these tools will allow implementation in any external platform, particularly in start-up Open Access publishers.