Paper by Abdelghani Maddi and David Sapinho: “Shadow libraries, also known as ”pirate libraries”, are online collections of copyrighted publications that have been made available for free without the permission of the copyright holders. They have gradually become key players of scientific knowledge dissemination, despite their illegality in most countries of the world. Many publishers and scientist-editors decry such libraries for their copyright infringement and loss of publication usage information, while some scholars and institutions support them, sometimes in a roundabout way, for their role in reducing inequalities of access to knowledge, particularly in low-income countries. Although there is a wealth of literature on shadow libraries, none of this have focused on its potential role in knowledge dissemination, through the open access movement. Here we analyze how shadow libraries can affect researchers’ citation practices, highlighting some counter-intuitive findings about their impact on the Open Access Citation Advantage (OACA). Based on a large randomized sample, this study first shows that OA publications, including those in fully OA journals, receive more citations than their subscription-based counterparts do. However, the OACA has slightly decreased over the seven last years. The introduction of a distinction between those accessible or not via the Scihub platform among subscription-based suggest that the generalization of its use cancels the positive effect of OA publishing. The results show that publications in fully OA journals are victims of the success of Sci-hub. Thus, paradoxically, although Sci-hub may seem to facilitate access to scientific knowledge, it negatively affects the OA movement as a whole, by reducing the comparative advantage of OA publications in terms of visibility for researchers. The democratization of the use of Sci-hub may therefore lead to a vicious cycle, hindering efforts to develop full OA strategies without proposing a credible and sustainable alternative model for the dissemination of scientific knowledge…(More)”.
Artificial Intelligence, Climate Change and Innovative Democratic Governance
Paper by Florian Cortez: “This policy-oriented article explores the sustainability dimension of digitalisation and artificial intelligence (AI). While AI can contribute to halting climate change via targeted applications in specific domains, AI technology in general could also have detrimental effects for climate policy goals. Moreover, digitalisation and AI can have an indirect effect on climate policy via their impact on political processes. It will be argued that, if certain conditions are fulfilled, AI-facilitated digital tools could help with setting up frameworks for bottom-up citizen participation that could generate the legitimacy and popular buy-in required for speedy transformations needed to reach net zero such as radically revamping the energy infrastructure among other crucial elements of the green transition. This could help with ameliorating a potential dilemma of voice versus speed regarding the green transition. The article will further address the nexus between digital applications such as AI and climate justice. Finally, the article will consider whether innovative governance methods could instil new dynamism into the multi-level global climate regime, such as by facilitating interlinkages and integration between different levels. Before implementing innovative governance arrangements, it is crucial to assess whether they do not exacerbate old or even generate new inequalities of access and participation…(More)”
Open Science and Data Protection: Engaging Scientific and Legal Contexts
Editorial Paper of Special Issue edited by Ludovica Paseri: “This paper analyses the relationship between open science policies and data protection. In order to tackle the research data paradox of the contemporary science, i.e., the tension between the pursuit of data-driven scientific research and the crisis of repeatability or reproducibility of science, a theoretical perspective suggests a potential convergence between open science and data protection. Both fields regard governance mechanisms that shall take into account the plurality of interests at stake. The aim is to shed light on the processing of personal data for scientific research purposes in the context of open science. The investigation supports a threefold need: that of broadening the legal debate; of expanding the territorial scope of the analysis, in addition to the extra-territoriality effects of the European Union’s law; and an interdisciplinary discussion. Based on these needs, four perspectives are then identified, that encompass the challenges related to data processing in the context of open science: (i) the contextual and epistemological perspectives; (ii) the legal coordination perspectives; (iii) the governance perspectives; and (iv) the technical perspectives…(More)”.
Surveys Provide Insight Into Three Factors That Encourage Open Data and Science
Article by Joshua Borycz, Alison Specht and Kevin Crowston: “Open Science is a game changer for researchers and the research community. The UNESCO Open Science recommendations in 2021 suggest that the practice of Open Science is a win-win for researchers as they gain from others’ work while making contributions, which in turn benefits the community, as transparency of conclusions and hence confidence in new knowledge improves.
Over a 10-year period Carol Tenopir of DataONE and her team conducted a global survey of scientists, managers and government workers involved in broad environmental science activities about their willingness to share data and their opinion of the resources available to do so (Tenopir et al., 2011, 2015, 2018, 2020). Comparing the responses over that time shows a general increase in the willingness to share data (and thus engage in open science).
A higher willingness to share data corresponded with a decrease in satisfaction with data sharing resources across nations.
The most surprising result was that a higher willingness to share data corresponded with a decrease in satisfaction with data sharing resources across nations (e.g., skills, tools, training) (Fig.1). That is, researchers who did not want to share data were satisfied with the available resources, and those that did want to share data were dissatisfied. Researchers appear to only discover that the tools are insufficient when they begin the hard work of engaging in open science practices. This indicates that a cultural shift in the attitudes of researchers needs to precede the development of support and tools for data management…(More)”.
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
Paper by Fabrizio Dell’Acqua et al: “The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company. After establishing a performance baseline on a similar task, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. We suggest that the capabilities of AI create a “jagged technological frontier” where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI. For each one of a set of 18 realistic consulting tasks within the frontier of AI capabilities, consultants using AI were significantly more productive (they completed 12.2% more tasks on average, and completed task 25.1% more quickly), and produced significantly higher quality results (more than 40% higher quality compared to a control group). Consultants across the skills distribution benefited significantly from having AI augmentation, with those below the average performance threshold increasing by 43% and those above increasing by 17% compared to their own scores. For a task selected to be outside the frontier, however, consultants using AI were 19 percentage points less likely to produce correct solutions compared to those without AI. Further, our analysis shows the emergence of two distinctive patterns of successful AI use by humans along a spectrum of human-AI integration. One set of consultants acted as “Centaurs,” like the mythical halfhorse/half-human creature, dividing and delegating their solution-creation activities to the AI or to themselves. Another set of consultants acted more like “Cyborgs,” completely integrating their task flow with the AI and continually interacting with the technology…(More)”.
Citizens call for sufficiency and regulation — A comparison of European citizen assemblies and National Energy and Climate Plans
Paper by Jonas Lage et al: “There is a growing body of scientific evidence supporting sufficiency as an inevitable strategy for mitigating climate change. Despite this, sufficiency plays a minor role in existing climate and energy policies. Following previous work on the National Energy and Climate Plans of EU countries, we conduct a similar content analysis of the recommendations made by citizen assemblies on climate change mitigation in ten European countries and the EU, and compare the results of these studies. Citizen assemblies are representative mini-publics and enjoy a high level of legitimacy.
We identify a total of 860 mitigation policy recommendations in the citizen assemblies’ documents, of which 332 (39 %) include sufficiency. Most of the sufficiency policies relate to the mobility sector, the least relate to the buildings sector. Regulatory instruments are the most often proposed means for achieving sufficiency, followed by fiscal and economic instruments. The average approval rate of sufficiency policies is high (93 %), with the highest rates for regulatory policies.
Compared to National Energy and Climate Plans, the citizen assembly recommendations include a significantly higher share of sufficiency policies (factor three to six) with a stronger focus on regulatory policies. Consequently, the recommendations can be interpreted as a call for a sufficiency turn and a regulatory turn in climate mitigation politics. These results suggest that the observed lack of sufficiency in climate policy making is not due to a lack of legitimacy, but rather reflects a reluctance to implement sufficiency policies, the constitution of the policy making process and competing interests…(More)”.
Artificial intelligence in local governments: perceptions of city managers on prospects, constraints and choices
Paper by Tan Yigitcanlar, Duzgun Agdas & Kenan Degirmenci: “Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI is on the rise in urban policy circles, there is limited understanding and lack of empirical studies on the city manager perceptions concerning urban AI systems. Bridging this gap is the rationale of this study. The methodological approach adopted in this study is twofold. First, the study collects data through semi-structured interviews with city managers from Australia and the US. Then, the study analyses the data using the summative content analysis technique with two data analysis software. The analysis identifies the following themes and generates insights into local government services: AI adoption areas, cautionary areas, challenges, effects, impacts, knowledge basis, plans, preparedness, roadblocks, technologies, deployment timeframes, and usefulness. The study findings inform city managers in their efforts to deploy AI in their local government operations, and offer directions for prospective research…(More)”.
Data Commons
Paper by R. V. Guha et al: “Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be ‘joined’ easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work…(More)”.
Evidence-based policymaking in the legislatures
Blog by Ville Aula: “Evidence-based policymaking is a popular approach to policy that has received widespread public attention during the COVID-19 pandemic, as well as in the fight against climate change. It argues that policy choices based on rigorous, preferably scientific evidence should be given priority over choices based on other types of justification. However, delegating policymaking solely to researchers goes against the idea that policies are determined democratically.
In my recent article published in Policy & Politics: Evidence-based policymaking in the legislatures we explored the tension between politics and evidence in the national legislatures. While evidence-based policymaking has been extensively studied within governments, the legislative arena has received much less attention. The focus of the study was on understanding how legislators, legislative committees, and political parties together shape the use of evidence. We also wanted to explore how the interviewees understand timeliness and relevance of evidence, because lack of time is a key challenge within legislatures. The study is based on 39 interviews with legislators, party employees, and civil servants in Eduskunta, the national Parliament of Finland.
Our findings show that, in Finland, political parties play a key role in collecting, processing, and brokering evidence within legislatures. Finnish political parties maintain detailed policy programmes that guide their work in the legislature. The programmes are often based on extensive consultations with expert networks of the party and evidence collection from key stakeholders. Political parties are not ready to review these programmes every time new evidence is offered to them. This reluctance can give the appearance that parties do not want to follow evidence. Nevertheless, reluctance is oftens necessary for political parties to maintain stable policy platforms while navigating uncertainty amidst competing sources of evidence. Party positions can be based on extensive evidence and expertise even if some other sources of evidence contradict them.
Partisan expert networks and policy experts employed by political parties in particular appear to be crucial in formulating the evidence-base of policy programmes. The findings suggest that these groups should be a new target audience for evidence brokering. Yet political parties, their employees, and their networks have rarely been considered in research on evidence-based policymaking.
Turning to the question of timeliness we found, as expected, that use of evidence in the Parliament of Finland is driven by short-term reactiveness. However, in our study, we also found that short-term reactiveness and the notion of timeliness can refer to time windows ranging from months to weeks and, sometimes, merely days. The common recommendation by policy scholars to boost uptake of evidence by making it timely and relevant is therefore far from simple…(More)”.
The Adoption and Implementation of Artificial Intelligence Chatbots in Public Organizations: Evidence from U.S. State Governments
Paper by Tzuhao Chen, Mila Gascó-Hernandez, and Marc Esteve: “Although the use of artificial intelligence (AI) chatbots in public organizations has increased in recent years, three crucial gaps remain unresolved. First, little empirical evidence has been produced to examine the deployment of chatbots in government contexts. Second, existing research does not distinguish clearly between the drivers of adoption and the determinants of success and, therefore, between the stages of adoption and implementation. Third, most current research does not use a multidimensional perspective to understand the adoption and implementation of AI in government organizations. Our study addresses these gaps by exploring the following question: what determinants facilitate or impede the adoption and implementation of chatbots in the public sector? We answer this question by analyzing 22 state agencies across the U.S.A. that use chatbots. Our analysis identifies ease of use and relative advantage of chatbots, leadership and innovative culture, external shock, and individual past experiences as the main drivers of the decisions to adopt chatbots. Further, it shows that different types of determinants (such as knowledge-base creation and maintenance, technology skills and system crashes, human and financial resources, cross-agency interaction and communication, confidentiality and safety rules and regulations, and citizens’ expectations, and the COVID-19 crisis) impact differently the adoption and implementation processes and, therefore, determine the success of chatbots in a different manner. Future research could focus on the interaction among different types of determinants for both adoption and implementation, as well as on the role of specific stakeholders, such as IT vendors…(More)”.