Abstract Collaboration between universities and non-academic organisations plays a vital role in addressing current societal demands. This study explored how such partnerships influence the productivity of traditional academic research by focusing on public universities and scientific teams. It examined the bidirectionality between scientific output and non-academic collaboration and the moderating effect of team knowledge diversity. Hierarchical multiple regression analysis on a sample of 277 Spanish scientific teams confirmed the bidirectional link between research productivity and non-academic collaborations—represented by an inverted U-shaped curve—and revealed a surprising negative moderating effect of team knowledge diversity. These results offer valuable insights for networks and research team management, particularly in balancing research productivity and academic engagement with society through non-academic collaborations.

Abstract This paper introduces a document type classifier with the purpose to optimise the distinction between research and non-research journal publications in OpenAlex. Based on open metadata, the classifier can identify non-research or editorial content within a set of classified articles and reviews (e.g. paratext , abstracts , editorials , letters ), which for example is relevant for bibliometric studies, university rankings and academic procedures. In this respect, OpenAlex shows issues in classifying journal research contributions, tending to overestimate them compared to other databases, due to the promotion of non-research items to research items. The classifier presented in this study achieves an F1-score of 0.95, indicating a potential improvement in the data quality of bibliometric research in OpenAlex when applying the classifier on real data. In total, 4,589,967 out of 42,701,863 articles and reviews could be reclassified as non-research contributions by the classifier, representing a share of 10.75%.

Abstract Measures of interdisciplinarity are often used to analyse research bibliometrically, yet little is known about whether these indicators capture how researchers themselves perceive interdisciplinarity. Against this background, the objective of this paper is to compare bibliometrically constructed indexes of interdisciplinarity with authors’ self-assessments of the interdisciplinarity of their own papers, thereby providing knowledge into how well these indicators correspond with researchers’ own perceptions. The bibliometric interdisciplinarity measures analyzed include the Shannon entropy, 2 D S , and DIV* indicators. The data analyzed in the study are derived from two separate questionnaire surveys, in which authors were asked about specific articles they had published. In total, the dataset consisted of more than 3300 articles, spanning all fields from the Humanities and Social sciences to the Natural sciences and Engineering. At the level of individual publications, bibliometric indicators often do not correspond with authors’ own assessments of interdisciplinarity. Although all analysed interdisciplinarity indicators show a statistically significant association with author assessments, the relationships are weak: correlation coefficient (Spearman’s rho) for the three bibliometric interdisciplinarity measures ranged from 0.130 to 0.175, highlighting the need for caution when relying solely on such measures for assessing research.

Abstract Governments around the world have encouraged science-industry linkages (SILs) to support new technological discoveries and the foundations for a country’s competitiveness through knowledge transfer. However, when identifying SILs, researchers mainly rely on co-patenting, leading to a distorted picture of the prevalence and types of SILs. To analyse how web mining may help SIL researchers establish a more comprehensive understanding of SILs, we rely on a dataset of 995,438 scraped news items from Chinese universities. Our analysis includes a comparison of web mining and co-patenting, which we document in an extensive literature review as the key identification strategy of SILs in prior research. We find that web mining allows us to identify substantially more firms and research linkages between science institutes (universities and public research institutes), and that firms’ characteristics tend to be different. Thus, web mining may be considered as a complementary identification strategy to co-patenting. Further, we find that Chinese firms are embedded in the Chinese innovation system by a diverse set of linkages (visiting, donation, recruitment and teaching), suggesting a rich network of knowledge transfer channels. Finally, we show heterogeneity in the linkage formation patterns of science institutes, with a small group of universities being highly specialized in distinct linkages, and a large group of rather inactive universities. We grant open access to data resources to encourage research in these areas.

Abstract This study examines how seemingly neutral bibliometric software performs geopolitical work by distorting national research metrics. Using a preregistered replication of 44 bibliometric studies, this research reveals how an operational assumption about what counts as a national entity, implemented in just two lines of code in a widely used open-source package, removed Taiwanese scientific contributions in 84% of the replicated studies while simultaneously inflating the academic output assigned to China. Evidence suggests the majority of the software's users were unaware of this reclassification, which is estimated to affect nearly 2,000 studies cited over 15,800 times. The results demonstrate that neither high user skill nor algorithmic transparency is sufficient to prevent bias, highlighting how software infrastructure can become a powerful, yet often invisible, actor in international disputes. The findings underscore the need for greater scrutiny of algorithms, not merely for technical accuracy, but to reveal how they can compel researchers to unknowingly adopt and amplify specific geopolitical stances.