75 citations · 215 across the 24 of their papers we have counts for
8 papers · 1 filter
Scoring Grant Applications with Large Language Models
Mike Thelwall, David Pride, Francesco Osborne +4
Purpose: Assessing grant applications is time-consuming and difficult, adding to the overall burden of academic peer review. Whilst funders are exploring whether AI can help, there…
COCI: Conference Organisers and Content Identifier
Angelo Salatino, Francesco Osborne, Alexis Vizcaino +2
Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from modern Scholarly Knowledge Graphs. T…
A Pathway for Assessing Grey Literature: Leveraging AI to Extract Conference Metadata and Organiser Information from Calls for Papers
Angelo Salatino, Francesco Osborne, Alexis Vizcaino +2
Despite its importance, grey literature, including Calls for Papers (CfPs), remains largely overlooked in Metascience and Scientometric analysis due to its unstructured, highly het…
Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
Tanay Aggarwal, Angelo Salatino, Francesco Osborne +1
Knowledge Organization Systems like Ontologies and taxonomies are fundamental for structuring scientific knowledge, yet their manual curation presents a persistent bottleneck in kn…
Representing Research Attention as Contextually Structured Flows
Jessica Rodrigues, Angelo Salatino, Gard Jenset +1
Research metrics use attention as evidence of societal impact. Yet attention serves as evidence only once interpreted, and its meaning depends on its contextual structure, not on v…
Research Entity Extraction and Topic Detection from UKRI Grant Proposals
Xingran Ruan, Angelo Salatino, Rosa Filgueira +4
This paper presents preliminary findings from a UKRI-funded Metascience project comparing three LLM-based approaches, GPT-4o, Mistral, and a bespoke algorithm, DSIT-Taxonomies, for…
Are Researchers Being Replaced by Artificial Intelligence?
Angelo A. Salatino, Ansgar Scherp, Christin Katharina Kreutz +1
A Nature survey from 2023 involving 1,600 researchers shows that scientists are ``concerned, as well as excited, by the increasing use of artificial-intelligence tools in research.…
How Do LLMs Encode Scientific Quality? An Empirical Study Using Monosemantic Features from Sparse Autoencoders
Michael McCoubrey, Angelo Salatino, Francesco Osborne +1
In recent years, there has been a growing use of generative AI, and large language models (LLMs) in particular, to support both the assessment and generation of scientific work. Al…