ABSTRACT 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.

ABSTRACT Purpose Ranking scientific papers is an important issue for all disciplines. Although this research problem has been intensively studied, the best ranking algorithm is still to be discovered. Some existing studies have proposed evaluation algorithms based on heterogeneous scholarly networks to measure the influence of papers. However, these algorithms have not fully considered the weighted characteristics of different scholarly networks, which can lead to biased ranking results. Evaluation algorithms based on weighted heterogeneous scholarly networks need to be further developed to address this limitation. Design/methodology/approach We propose a weighted heterogeneous network-based ranking algorithm to evaluate the impact of scientific papers, considering the mutual reinforcement relationship among the influences of different academic entities. The weighted heterogeneous scholarly network we constructed considers factors such as the similarity between papers, the number of authors in the papers, and the number of papers published by the authors, which can effectively reflect the relationships among papers, authors, and journals. Findings We applied the proposed algorithm to the American Physical Society (APS) dataset and verified the effectiveness of this method. The empirical results indicate that, compared with other mutual enhancement algorithms, our proposed algorithm can better identify recognized high-impact papers and perform well in evaluating both author and journal impacts. Research limitations The proposed algorithm has only been verified in the domain of physics, and further validation of the algorithm’s effectiveness is needed in other disciplines. Practical implications This research can deepen our understanding of impact evaluation and help propose better evaluation methods. The proposed algorithm can be applied to identify important papers in the field of physics and recommend them to relevant scientists. Originality/value This study considers the weighted characteristics of different academic networks more comprehensively and, on this basis, proposes a paper impact evaluation algorithm based on weighted heterogeneous networks by leveraging the mutual reinforcement relationship of the influence of different academic entities.

ABSTRACT 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.

ABSTRACT Metrics for surgery, obstetrics, and anesthesia are crucial for implementing programs and monitoring progress toward safe and effective healthcare systems in pursuit of universal healthcare. A suite of metrics put forward by the Lancet Commission on Global Surgery has been adopted in principle by global health, anaesthesia, and surgical leadership in diverse settings. However, barriers to implementation limit their value. Barriers include inconsistencies in definitions and methodologies such as inadequate consideration given to sampling frames, representativeness, categorizations, missing data, and data collection infrastructure. Using the Utstein consensus methodology, we developed a uniform approach to collecting metrics in surgery, obstetrics, and anesthesia. We created a standard toolkit to facilitate the rapid implementation of the Lancet Commission indicators. The metadata and data dictionaries allow a standardized assessment of preparedness for, delivery of, and the effect of care at the population level.

ABSTRACT 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.

ABSTRACT 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.

ABSTRACT 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.

ABSTRACT Purpose This research endeavors to investigate the impact of open government data on corporate investment, emphasizing the exploration of underlying mechanisms, heterogeneous effects, and implications for investment efficiency. Utilizing the implementation of government data open platforms as a quasi-natural experiment, this study aims to elucidate how public data transparency affects firms’ investment decisions and resource allocation. Design/methodology/approach This study employs a staggered Difference-in-Differences (DID) model as its principal methodological framework. This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time. Findings The findings indicate that open government data substantially enhance corporate investment levels. A mechanistic analysis identifies three principal channels through which this effect is mediated: alleviation of overall financing constraints, reduction of financing costs, and expansion of the financing scale. A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises, high-tech firms, and companies experiencing elevated levels of macroeconomic uncertainty. Moreover, the transparency of government data improves the responsiveness of corporate investment to emerging opportunities, thereby augmenting the overall efficiency of corporate investment. Research limitations This study primarily examined the influence of government data transparency on corporate investment, while not accounting for the effects of macroeconomic variability, internal corporate governance frameworks, and industry-specific regulatory policies. Practical implications Government open data platforms can effectively boost corporate investment and resource allocation. Policymakers should focus on improving the quality and accessibility of these data, especially in areas with high economic uncertainty, to support business investments. Firms, particularly high-tech and financially constrained firms, can use open data to ease capital limitations and find investment opportunities. Regulators should promote data transparency to enhance economic vitality through efficient corporate investments. Originality/value This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment, a subject that has been relatively underexplored empirically. By employing a quasi-natural experiment centered on the implementation of government data platforms, this study adopts a robust methodological approach to address endogeneity issues, thereby advancing methodological rigor in investigations of public data governance and corporate behavior.

ABSTRACT Purpose This paper focuses on scientific journals’ policies on open access and open science. The subject has gained increasing relevance, driven by the need for more-democratic access to knowledge and improved research visibility, which require eliminating the financial, legal, and technical barriers that restrict access to scientific output. Design/methodology/approach This paper uses the findings of FECYT’s 2023 Assessment of the Editorial and Scientific Quality of Spanish Scientific Journals, with 254 participating journals, as its case study. Open science indicators assess the transparency of policies on content access, reuse, openness, and reproducibility. Nonparametric tests analyse the relationship between the indicators and the dimensions of publisher type and subject area. Findings High compliance rates are found for indicators related to publication licences and intellectual property rights. Only 37% of the journals examined post their editorial policy on Sherpa Romeo. Ninety-four percent publish open access. However, open peer review is rarely applied (0.38% of the journals). Journals in Communication, Information and Scientific Documentation, Fine Arts, Education Science, and Biomedical Sciences have high compliance percentages. Most journals (83%) are institutional, with universities and associations generally exhibiting better results. Research limitations This study is based on specific indicators that do not cover all the factors that influence the transition toward open science; for example, editorial culture and technological infrastructure are not envisaged. Furthermore, differences in open science implementation are identified between disciplinary areas and between publisher types, but the underlying causes of these differences are not thoroughly investigated. Future research could address these points for a fuller understanding. Practical implications This study highlights the need for journals to improve transparency by adopting open peer review and clear policies. These changes enhance accessibility and credibility, fostering inclusive knowledge dissemination. Institutions and policymakers should support these efforts to boost research impact. Originality/value This study offers insights into open science practices in Spanish journals, a growing academic topic. Its originality lies in examining open science indicators across disciplines and publishers. By identifying strengths and gaps, the study helps journals enhance transparency.

ABSTRACT 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.