Purpose: This paper investigates the impact of 100 % article processing charge (APC) waivers introduced by the five largest commercial publishers - Elsevier, SAGE, Springer Nature, Taylor & Francis, and Wiley - on the participation of Ukrainian researchers in fully Gold Open Access (OA) publishing during 2019-2024. It aims to assess whether the temporary removal of financial barriers during wartime led to measurable changes in Ukraine’s OA publication activity.
Design/methodology/approach: Bibliometric data were retrieved from the Web of Science Core Collection, focusing exclusively on fully Gold OA journals published by the five selected publishers. The analysis covers Ukrainian-affiliated papers published between 2019 and 2024, examining annual publication dynamics, publisher-specific distributions, disciplinary profiles, and cross-country comparisons with Poland, the Czech Republic, and Hungary.
Findings: The number of Ukrainian-authored articles in the selected Gold OA journals increased sharply after 2022, rising by more than 50 % between 2022 and 2023. The strongest growth occurred in journals by Springer Nature and Elsevier and in medical and applied sciences. While the surge correlates with the introduction of full APC waivers, additional factors, such as international collaborations and targeted research funding, also contributed.
Research limitations: The study cannot verify waiver use at the individual article level, as publishers do not disclose this information. It relies on WoS metadata and excludes hybrid, diamond, and non-commercial OA journals. Consequently, results should be interpreted as indicative rather than definitive evidence of causal relationships.
Practical implications: The findings highlight that well-targeted publishing support, such as temporary APC waivers, can sustain scholarly visibility during crises. However, without institutional mediation, awareness campaigns, and broader investment in research capacity, such measures offer only partial solutions to systemic inequities in the APC-based publishing model.
Originality/value: This is the first empirical assessment of the wartime APC-waiver policies for Ukrainian researchers. By isolating a unique natural experiment involving five global publishers, the study contributes new evidence to discussions on equity, resilience, and sustainability in Open Access publishing under crisis conditions.

Purpose: This study aims to analyze the key technologies in Industry-University-Research (IUR) cooperation within higher education institutions, deepen the understanding of the mechanisms of IUR cooperation and the process of technological innovation, and reveal the dynamic evolution patterns and driving mechanisms of key technologies in IUR cooperation alliance networks at different stages. It also provides clear directions and strategic recommendations for cooperation among universities, enterprises, and research institutions.
Methodology: This study uses patents applied for through IUR cooperation by Chinese Double First-Class universities from 2015 to 2024 as the data basis and employs the Louvain algorithm to divide IUR cooperation applicants. Subsequently, a Technology-Applicant network is constructed at two-year intervals, and key technologies are extracted using network information entropy. The evolution paths of technological characteristics are then thoroughly analyzed. Finally, the study proposes three hypotheses and employs the Exponential Random Graph Model (ERGM) to systematically elucidate the endogenous driving mechanisms of key technology characteristics in the applicant.
Findings: Over the past decade, IUR cooperation in Chinese Double First-Class universities has undergone a transformation from single technological fields to the deep integration of multiple technological fields and from traditional application areas to emerging ones. The knowledge depth, knowledge width, and knowledge combination capabilities of IUR applicants, as core independent variables, have had varying impacts on network formation across different time periods. Among them, knowledge combination capability has played a significant role in promoting network formation.
Research limitations: On the one hand, this study mainly focuses on the Double First-Class universities in China and does not cover other types of universities. On the other hand, while the study mainly focuses on the analysis of the IUR technology network, the analysis of the cooperation network between applicants is still insufficient.
Practical implications: This study provides practical guidance for optimizing IUR cooperation networks by emphasizing the integration of multiple technological fields, balancing knowledge depth and width, enhancing knowledge combination ability, and optimizing the internal network structure. These measures help to strengthen the stability and efficiency of cooperation networks, boost innovative outcomes, and provide strong support for scientific and technological progress as well as economic development.
Originality/value: This study examines the evolution of key technologies and their impact on IUR cooperation networks in China over ten years. It shows a shift from single to multiple technological fields and from traditional to emerging applications, highlighting Chinese global competitiveness. Core variables like knowledge depth, width, and combination ability differently affect network formation over time, with knowledge combination being consistently significant. Network structural characteristics also crucially regulate stability and efficiency. The findings offer theory-based practical guidance to optimize these networks.

Purpose: This study synthesizes existing research on scientific prizes and outlines a framework for understanding how reward systems shape careers, credit allocation, and field trajectories.
Design/methodology/approach: We conducted a comprehensive literature review integrating scientometrics, the sociology of science, and economics to synthesize theoretical frameworks and empirical evidence on prize mechanisms, effects, and governance.
Findings: Scientific prizes function as signals in status hierarchies, interventions that redirect attention across people and topics, and governance tools whose design determines equity and recognition outcomes. Empirical evidence reveals significant impacts on winners, collaborators, and research areas following prize awards. However, current prize systems exhibit systematic biases across demographics and institutions that reinforce existing inequalities.
Research limitations: Empirical research remains fragmented across disciplines and prize types. Long-term longitudinal and cross-cultural comparative studies are needed to establish universal versus context-specific mechanisms.
Practical implications: Achieving more equitable prize systems requires addressing structural barriers in nomination and selection processes, while carefully balancing trade-offs between accessibility, administrative capacity, and community trust.
Originality/value: This study provides a comprehensive interdisciplinary framework for scientific prizes, offering evidence-based recommendations for prize design that better serve scientific progress and equity goals.

Purpose: Overton, a global policy index, provides new opportunities to study the interactions between science and policy. This study aims to characterize the presence of scholarly and policy references in Overton-indexed policy documents and examine their distribution across key bibliographic dimensions, thereby assessing Overton’s potential as a data source for policy metrics.
Design/methodology/approach: We analyze a dataset of approximately 17.5 million policy documents from Overton, incorporating metadata such as publication year, policy source, country, language, subject area, and policy topic. Descriptive statistics are employed to assess the presence and distribution of reference data across these dimensions.
Findings: Overton indexes a substantial volume of policy documents and identifies considerable reference data within them: 7.7% of documents contain scholarly references and 10.6% contain policy references. However, the presence of references varies significantly across publications years, source types, countries, languages, subject areas, and policy topics, indicating coverage biases that may affect interpretations of policy impact.
Research limitations: The analysis is based on the Overton database as of June 2025. As Overton is regularly updated, the distribution patterns of indexed documents and references may evolve over time.
Practical implications: The findings offer insights into the opportunities and constraints of using Overton for investigating evidence-based policymaking and for assessing the policy uptake of research outputs in the context of research evaluation.
Originality/Value: This is the first large-scale study to systematically examine the distribution of reference data in Overton. It contributes a foundational understanding of this emerging source for policy metrics, highlighting both its potential applications and limitations, and underlining the importance of addressing current coverage imbalances.

Purpose: Rank aggregation plays a crucial role in various academic and practical applications. However, accurately assessing the quality of ranking data remains a critical challenge. This study aims to propose methods for assessing the quality of ranking data from the perspective of its distribution.
Design/methodology/approach: This study adopts a network science perspective, transforming ranking data into a network and evaluating its quality using network structural entropy. In addition, we extended three commonly used ranking data generation models to produce ranking data with different distribution characteristics. Finally, the effectiveness of the proposed methods was validated using both synthetic and real-world data.
Findings: Through experiments, we validated the effectiveness of the proposed methods in assessing the quality of ranking data from the perspective of distribution. Additionally, the study revealed the following: (1) simply increasing the number of input rankings does not necessarily improve data quality; (2) when dealing with unevenly distributed ranking data, different aggregation methods exhibit significant differences in performance; and (3) increasing the length of input rankings can mitigate the decline in aggregation effectiveness caused by the uneven probability of each object being ranked.
Research limitations: (1) This study focuses on the impact of distribution characteristics on the quality of ranking data, without considering the effect of disagreements within the data; (2) although the proposed methods have been validated on synthetic and real-world datasets, their generalizability may still require further testing on more diverse datasets.
Practical implications: The methods proposed in this study enables researchers and information managers to more accurately assess the quality of input data before performing rank aggregation, thereby enhancing decision-making reliability.
Originality/value: This study proposes two novel methods from the perspective of network science to address the challenge of data quality assessment in rank aggregation, providing both theoretical support and practical insights for related fields.

Purpose: Prior Information Retrieval (IR) research synthesizes progress from individual studies, yet academia-industry collaboration dynamics remain unexplored. This study investigates: (1) productivity patterns and venues, (2) citations-downloads relationships, (3) topic evolution, and (4) collaboration trends.
PDesign/methodology/approach: We perform an analysis of 53,471 ACM IR papers (2000-2018) using bibliometrics and DistilBERT topic modeling.
Findings: We find that industry-involved papers preferred WWW/CIKM venues; collaborations dominated RecSys/CSCW. We see that academia-industry collaborations achieved the highest download-to-citation conversion rates. Academia focused on algorithms; industry on applications; collaborations bridged both with rising human-centered themes.
Research implications: This is a pioneering large-scale bibliometrics revealing collaboration’s impact on IR knowledge evolution and provides a methodological framework for cross-sector analysis.
Practical implications: The paper identifies optimal venues (RecSys/CSCW) for partnerships and guides joint initiatives (shared datasets, grants) to bridge academia-industry divides and enhance research translation.
Originality/value: This is the first large-scale bibliometric analysis of IR academia-industry collaboration. The paper finds many novel insights, including the fact that collaboration boosts citation efficiency, enables complementary specialization, and drives topic convergence.

Purpose: Examining the alignment (or “fit”) of China’s science and technology talent policies provides valuable insights into the challenges and shortcomings in supporting talent development, thereby offering a foundation for enhanced policy design and support.
Design/methodology/approach: This study introduces a policy fit analysis framework, which decomposes policy fit into three dimensions: consistency fit, embeddedness fit, and compensatory fit. By employing quantitative research methods, the study conducts a multidimensional analysis of China’s science and technology talent policies over the period from 2014 to 2023.
Findings: The findings indicate that, after a decade of evolution, China’s policy system for science and technology talent has largely matured into a relatively stable framework, with policy fit demonstrating an upward trend over time. However, several challenges persist. For instance, the policy system places a disproportionate emphasis on talent cultivation and development, while comparatively fewer policies address the introduction, aggregation, and strategic planning of talent. Additionally, there are observable gaps between policy objectives and actual outcomes, as well as a misalignment between policy supply and the demands of talent development.
Research limitations: The framework of policy fit analysis proposed by the study can only analyze policies at the same level, but it cannot conduct cross-level analysis. In the empirical analysis, the policy texts analyzed were limited to publicly available documents.
Practical implications: The findings provide new perspectives and methodologies for policy evaluation, expanding the scope of existing policy analysis, and also offer meaningful guidance for policymakers and relevant administrative personnel.
Originality/value: This paper introduces, for the first time, a policy fit analysis framework, addressing a gap in the study of policy alignment.

Purpose: Since peer review for funding decisions is crucial to the scientific system, we direct the reader towards new ideas related to research funding and the associated peer review process.
Design/methodology/approach: We describe the overall structure of the funding review system and explore the expectations of its various key stakeholders. An examination of testing across the review processes of different funding agencies revealed several issues in the current system. We then summarize the efforts to explore potential solutions. Before concluding, we also discuss recent initiatives, including partial lottery mechanisms, distributed peer review, and methods for identifying originality in proposals by examining areas of non-consensus among reviewers and applicants.
Findings: It is difficult to test whether the funding peer review system functions as expected. Moreover, when the peer-review process was replicated across different review groups, the inter-rater problem, where two or more well-intentioned reviewers reached divergent conclusions, was found to be widespread in funding evaluations. At its core, this issue stems from substantive disagreements among reviewers, which can introduce bias into the process. As a result, organizing a peer-review system that is fair, valid, and reliable for funding decisions is particularly challenging. The contemporary organization of the funding review system does not guarantee that it can fulfill its purpose. Consequently, scientists are looking to substantiate funding decisions with more scientific evidence. Some new initiatives have been proposed, which are either more interactive with a strictly organized procedure or are more random (or stochastic), leading to less bias.
Research limitations: For practical reasons, we were not able to discuss all, or at least the main, funders in the world.
Practical implications: Considering the various steps in peer review procedures for funding decisions may inspire the readers to suggest improvements to the existing system, resulting in reduced bias and greater equality among scientists.
Originality/value: Our work contributes to understanding peer review in funding contexts and to exploring possible reforms aimed at improving the existing system.