Publications

This paper proposes a novel ontology, called SciOnt, for representing scientometric indicators. Scientometrics is the field that studies the quantitative aspects of science and scientific phenomena. Scientometric indicators are used to evaluate scientific publications and research. However, many indicators and their diversity make them challenging to understand and use. SciOnt addresses this challenge by providing a formal and structured representation of scientometric indicator knowledge. It defines classes, relationships, and instances of these indicators. The ontology can be used for various purposes, including improving the analysis and comparison of scientometric indicators, facilitating the development of new and more efficient indicators, and enhancing the understanding and criticism of existing indicators. The paper details the methodology used to design SciOnt, including identifying and classifying scientometric indicators, determining competency questions, and constructing the ontology with Protégé 5.5 software. Finally, the paper evaluates the ontology and visualizes the SciOnt ontology structure using various plugins.

To address the academic integrity challenges arising from artificial intelligence technologies, this study has systematically investigated the policies governing the use of artificial intelligence-generated content (AIGC) in academic writing, specifically, those implemented by leading journals participating in Phase II of “Excellence Action Plan of China Scientific and Technological Journals Project” (hereinafter referred to as the AIGC use policy). The survey findings reveal a “spindle-shaped” distribution of policy stringency across leading English-language journals: only approximately 20% of these journals have adopted high-intensity policies classified as “strict” or “stringent”. The leading Chinese-language journals exhibit a pronounced policy gap concerning AIGC use policy; most journals lack relevant specific guidelines, and merely four journals have issued dedicated policy statements. Further analysis reveals that the technical affinity, ethical sensitivity, and research traditions inherent to a discipline intersect with the institutional logics underpinning publishing entities, such as their affiliations with international commercial conglomerates or scholarly societies. The interplay collectively shapes the form, availability, and regulatory effectiveness of AIGC use policies, yielding a current policy landscape characterized by both structural imbalance and delayed responsiveness. Based on the above, this manuscript proposes the establishment of a “stratified, discipline-specific, and dynamically collaborative” governance system. It advocates the strengthening of policy guidance tailored to disciplinary heterogeneity, accelerating institutional transformation across publishing entities, and instituting mechanisms for monitoring technological evolution and iteratively updating governance policies, thereby achieving a dynamic equilibrium between safeguarding the foundational principles of academic integrity and accommodating the intrinsic logic of technological advancement. Only through such an integrated, adaptive approach can China’s scientific and technical journals substantially enhance their academic governance capacity and effectively respond to the evolving demands of the artificial intelligence era.

In 2022, a coalition of 52 scholarly publishers representing more than 15,000 journals endorsed a standardised questionnaire on ethnic origins, ancestry, and race. This Opinion tests whether that questionnaire’s ancestry item follows the geographic rationale its own designers cite for it, using a documentary audit against the United Nations M49 geoscheme. The wording refers to ancestry and ethnic origins, but the response options record coarse geographic origin. The European categories make the problem especially visible. The instrument offers only “Western Europe” and “Eastern Europe”, omits Southern Europe, and lists Greece, Sweden, and the United Kingdom as being part of Western Europe, although UN M49 places Greece in Southern Europe and Sweden and the United Kingdom in Northern Europe. Several other options depart from M49 in distinct ways, including a label–example mismatch, deliberate combinations of adjacent subregions, and aggregation to a higher regional level. These inconsistencies do not validate M49 as an ancestry taxonomy. They show that the questionnaire uses a hybrid, insufficiently documented geography instead of following its stated rationale consistently. Public evidence is not sufficient to demonstrate that respondents interpret the resulting categories consistently across countries and languages. Publishers should define the construct being measured, separate collection from reporting, collect sufficient detail before aggregation, and test revised questions across countries and languages.