Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1210.0442

Citation analysis may severely underestimate the impact of clinical research as compared to basic research

Nees Jan van Eck·Ludo Waltman·Anthony F. J. van Raan·Robert J. M. Klautz·Wilco C. Peul
View original source ↗Read source document ↗Export metadata ↓

Abstract

Background: Citation analysis has become an important tool for research performance assessment in the medical sciences. However, different areas of medical research may have considerably different citation practices, even within the same medical field. Because of this, it is unclear to what extent citation-based bibliometric indicators allow for valid comparisons between research units active in different areas of medical research. Methodology: A visualization methodology is introduced that reveals differences in citation practices between medical research areas. The methodology extracts terms from the titles and abstracts of a large collection of publications and uses these terms to visualize the structure of a medical field and to indicate how research areas within this field differ from each other in their average citation impact. Results: Visualizations are provided for 32 medical fields, defined based on journal subject categories in the Web of Science database. The analysis focuses on three fields. In each of these fields, there turn out to be large differences in citation practices between research areas. Low-impact research areas tend to focus on clinical intervention research, while high-impact research areas are often more oriented on basic and diagnostic research. Conclusions: Popular bibliometric indicators, such as the h-index and the impact factor, do not correct for differences in citation practices between medical fields. These indicators therefore cannot be used to make accurate between-field comparisons. More sophisticated bibliometric indicators do correct for field differences but still fail to take into account within-field heterogeneity in citation practices. As a consequence, the citation impact of clinical intervention research may be substantially underestimated in comparison with basic and diagnostic research.

Explore related subjects

cs.DL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nees Jan van Eck, Ludo Waltman, Anthony F. J. van Raan, Robert J. M. Klautz, Wilco C. Peul. 2013-04-28. Citation analysis may severely underestimate the impact of clinical research as compared to basic research. https://doi.org/10.1371/journal.pone.0062395

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The illusion of neutrality in metric-based research evaluation

Venue prestige and citation counts are two widely used, albeit imperfect, signals of research quality. When the two signals conflict, evaluators must decide how much weight to assign each. Yet, it remains unknown how researchers across disciplines trade off these two signals when evaluating research outcome. To fill this gap, we surveyed 869 researchers using paired choices between hypothetical departmental hiring rules that assigned different weights to venue prestige and citation counts, asking which would produce better science. Among 795 respondents whose choices were largely internally consistent, choices placed nearly equal aggregate weight on the two signals. This apparent balance concealed substantial individual heterogeneity: nearly two in five respondents occupied the most venue-heavy or citation-heavy intervals. Moreover, respondents on average chose more citation-heavy rules than they believed their departments used in hiring. By revealing the subjective judgments that arise when research indicators conflict, our findings reinforce calls for greater caution when quantitative indicators are used to evaluate research.

cs.DL↗
arXiv2026

Scoring Grant Applications with Large Language Models

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 is no published research into the accuracy of Large Language Models (LLMs) for scoring contemporary grants. Design/methodology/approach: This study investigates whether six open-weight LLMs (Gemma 3 1B/4B/12B/27B, DeepSeek R1 32B, Qwen 3 32B) can give useful scores for 2267 recent UK Economic and Social Research Council (ESRC), and Engineering and Physical Sciences Research Council (EPSRC) grant applications, comparing them with scores from the original reviewers and funding panel members. Findings: Although the LLM scores are individually inaccurate, when averaged and converted to ranks they correlate positively with expert average scores. The best performing LLM, Gemma 3 27B (10 iterations with varied prompts), had moderate rank correlations with average reviewer scores (mean rho=0.26). Gemma 3 27B's average correlation with individual reviewers was 0.19, which is lower than the inter-reviewer mean correlation of 0.24, suggesting that it scores are slightly weaker than individual reviewer scores. Gemma 3 27B had weak rank correlations with average panel member scores (mean rho=0.17), with lower average correlations with individual panellists (mean rho=0.14), which is substantially lower than the inter-panellist correlation (mean rho=0.38). Whilst the correlations seem too weak to replace expert review at the final panel stage, LLM scores might help with the initial reviewing state, such as by helping identify the weakest proposals for fast-track desk rejections, to replace one human reviewer, or for triangulation to check for bias.

cs.DL↗
arXiv2026

Open Science, Closed Models: How Funding Shapes AI in Science

How funding shapes AI engagement in science is poorly understood despite its structural importance. We analyze 104,226 scientific papers (2018-2025), linking funding acknowledgments to how each paper engages with foundation models: whether it extends a model (fine-tunes or builds on it), uses one without modification, or references models only peripherally. Three findings emerge. First, funding source is associated with the character of engagement: public funding is associated with higher open-weight model engage- ment; private-only funding selectively enables extension with no robust openness association; mixed funding shows nominally the highest open-weight engagement rates in four of the seven largest disciplines. Second, papers acknowledging industry cloud credits are less likely to use open-weight models, consistent with credit programs steering research toward closed models. Third, industry collaboration carries an independent extension premium consistent with internal corporate resources flowing through coauthorship channels invisible to acknowledgment-based measures. This nexus concentrates in Computer Science and Global North collaborations; Global South research is largely excluded. As public funding contracts and industry compute provision expands, scientific AI work shifts toward closed proprietary infrastructure.

cs.DL↗