Sep 2026· ePrints Soton (University of Southampton)
scientometrics and bibliometrics research
Abstract
Bibliometrics is a statistical science which has a profound influence on behaviours and resource allocation across the global academic ecosystem. It affects the allocation of energies and resources by researchers, authors, journals, publishers, universities, corporations and governments. Bibliometrics is primarily a product of two major information systems: The Web of Science, from Clarivate Analytics, and SCOPUS from Elsevier BV of the Netherlands. Both products are hugely complex systems. They must be managed and organised in an ordered and structured manner to be effective, through policies which describe the rules of content accrual, processing and delivery to its customers. Trust and Quality Assurance are central to the societal and commercial value of bibliometric systems. The designers of SCOPUS, with which I am most familiar, determined at the outset in 2003-2004 that content accrual would be managed through an external board of advisors, the SCOPUS Content Selection Advisory Board (CSAB). I have been privileged to be a member of the CSAB as the Subject Chair for Medicine since the outset of the current CSAB programme in 2009. This role has engaged me in the development of the SCOPUS Title Evaluation Platform (STEP); the major expansion and diversification of SCOPUS content; the diversification of bibliometrics; the growth of open access publishing; the move from subscription based to article processing fee based commerce; and the massive growth of sophisticated publication fraud; and the emergence of Machine Learning and Artificial Intelligence systems. In this essay, I seek to describe the work of the Board in terms of the development of its policy framework over the formative period 2010-2011.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
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Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6