Publications

Ranking research groups plays a crucial role in various contexts, such as ensuring the fair allocation of research grants, assigning projects, and evaluating journal editorial boards. In this paper, we analyze the distribution of h-indexes within research groups and propose a single metric to quantify their overall performance, termed the α-index. This index integrates two complementary aspects: the homogeneity of members’ h-indexes, captured by the Gini coefficient (g), and the h-group, an extension of the individual h-index to groups. By combining both uniformity and collective research output, the α-index provides a consistent and equitable metric for comparative evaluation, essentially calculated as the average relative h-group multiplied by and normalized by the maximum value of this quantity across all analyzed groups. We describe the full procedure for computing the index and its components and illustrate its application to computer science conferences, where program committees are compared through a resampling procedure that ensures fair comparisons across groups of different sizes. Additional results are presented for postgraduate programs, further demonstrating the method’s applicability. Correlation analyses are used to establish rankings; however, our primary goal is to recommend a fairer index that reduces deviations from those currently used by governmental agencies to evaluate conferences and graduate programs. The proposed approach offers a more nuanced assessment than simply averaging members’ h-indexes and can be applied broadly–for example, to university departments and research councils–contributing to a more equitable distribution of research funding, an issue of increasing importance.

Recent decades have seen a rise of anti-science rhetoric, fueled by scientific scandals and failures of peer review, and the rise of trainable generative AI spreading misinformation. We argue, moreover, that the continued erosion of scientific authority also arises from inherent features in science and academia, including a reliance on publication as a method for gaining professional credibility and success. Addressing this multifaceted challenge necessitates a concerted effort across several key areas: strengthening scientific messaging, combating misinformation, rebuilding trust in scientific authority, and fundamentally rethinking academic professional norms. Taking these steps will require widespread effort, but if we want to rebuild trust with the public, we must make significant and structural changes to the production and dissemination of science.

Open Access (OA) has emerged as a pivotal driver shaping the dissemination scope and academic impact of research findings. To clarify the impact of publishing models such as open access on the citation performance of biochemical papers, this study selects 177,745 biochemistry professional papers included in the core collection of the Web of Science (WoS CC) as the research data; we conduct an analysis of citation and impact advantages in biochemistry research. Employing correlation analysis, baseline regression modeling, and two-way ANOVA, our analysis indicates that: OA publications in biochemistry exhibit notable citation and impact advantages, which are positively correlated with the degree of openness, and the key determinants of the OA advantage encompass funding sources, reference count, and publication region. At present, China accounts for a disproportionately small proportion of OA papers in this field. In the context of the open-science paradigm, Chinese academic journals must systematically address their developmental bottlenecks and formulate publication innovation strategies to enhance the quality of academic publishing.