Abstract Artificial intelligence is becoming part of the infrastructure through which scientific knowledge is produced, yet access to computing, data, advanced models and expertise remains highly uneven. This Perspective argues that, for emerging research systems, the central science-policy challenge is not simply AI adoption but scientific autonomy: the capacity to choose, understand, adapt, evaluate and govern the technologies on which research increasingly depends. Scientific autonomy is not technological self-sufficiency; it is strategic interdependence supported by domestic capabilities. Drawing on Brazil as an illustrative case, we propose four dimensions of autonomy—epistemic, infrastructural, human-capability and governance autonomy—and a six-part policy agenda based on shared computing, research-data infrastructure, AI literacy, interdisciplinary networks, adaptive governance and capability-building international cooperation. Without deliberate capacity-building, AI may accelerate discovery while deepening structural asymmetries between research systems.
Abstract The design of novel green industrial policies presents a coordinative challenge for national governments, crossing many policy domains. This paper focuses on the coordination of Science, Technology and Innovation (STI) policies and energy policies, asking: How do elected political leaders coordinate STI and energy policies on emerging energy innovations? We answer this question by considering the case of STI and energy policy coordination in the UK between 1997 and 2003, describing the political leadership of key members of the Blair government. The case illustrates that strong political leadership guided by a party-specific cross-cutting political-economic agenda (e.g. New Labour) can play a critical role in coordinating policy reforms across multiple domains. In the European context, the case provides a precedent for how STI and energy policies may be aligned to support European competitiveness, in that it points to the importance of both formal institutional reforms and informal political leadership.
Abstract ‘Citizen science,’ an umbrella term for researchers and public participants collaborating to produce scientific knowledge, has both produced data informing public policies and been included in policies guiding future scientific inquiries. Yet, its use and influence remain challenging. Research has found incorporating inclusivity, equity, and/or empowerment principles into citizen science can bolster its value to science and society. This scoping review of 127 citizen science publications, identified via the Web of Science and other sources, explores how these publications describe the application of these principles in citizen science, and their linkages to data quality and ethics. The results describe linkages between these three principles, data quality, ethics, and the achievement of citizen science aims. Incorporating these principles requires significant design choices, influencing the distribution of costs, risks, and responsibilities. The findings also highlight multiple gaps in knowledge and related policies regarding applying these principles in citizen science.
Abstract With artificial intelligence accelerating scientific discovery and automating complex research tasks, the rapid integration of AI in research is also creating a collective action problem that pits individual academic productivity against the long-term reliability of the scientific record. This introduces deeply destabilizing vulnerabilities into contemporary and future scholarship, characterized by fabricated references infiltrating reputable peer-reviewed venues and the imminent threat of model collapse as flawed, AI-generated literature pollutes tomorrow’s model-training datasets. These vulnerabilities threaten to permanently compromise the credibility of science as the most dependable knowledge engine we have, if the scientific community does not address them now through proper accountability and systemic governance frameworks.
Abstract Research on scientific collaboration typically examines domestic intercity networks and country-level international networks separately, leaving unclear whether the productivity associations of cities’ domestic network positions vary across national contexts. Using SciSciNet records in Library Science and Information Retrieval for 2000–2025, we estimate city and year fixed-effects regressions on 39,341 city-year observations covering 2,772 Functional Urban Areas in 116 countries. City research productivity has inverted U-shaped associations with both the focal city’s clustering coefficient and its average tie strength in the domestic intercity network. Both curves are steeper for cities in countries that span more structural holes in the international collaboration network. The ascending slopes are more positive, while the downturns beyond the turning points are sharper. These associational findings identify cross-level heterogeneity that city-only and country-only accounts cannot represent and offer conditional implications for science and innovation policy.
