Search arXivSearch

arXiv · 1201.3068

Metrics to evaluate research performance in academic institutions: A critique of ERA 2010 as applied in forestry and the indirect H2 index as a possible alternative

Abstract

Excellence for Research in Australia (ERA) is an attempt by the Australian Research Council to rate Australian universities on a 5-point scale within 180 Fields of Research using metrics and peer evaluation by an evaluation committee. Some of the bibliometric data contributing to this ranking suffer statistical issues associated with skewed distributions. Other data are standardised year-by-year, placing undue emphasis on the most recent publications which may not yet have reliable citation patterns. The bibliometric data offered to the evaluation committees is extensive, but lacks effective syntheses such as the h-index and its variants. The indirect H2 index is objective, can be computed automatically and efficiently, is resistant to manipulation, and a good indicator of impact to assist the ERA evaluation committees and to similar evaluations internationally.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jerome K. Vanclay, Lutz Bornmann. 2012-01-15. Metrics to evaluate research performance in academic institutions: A critique of ERA 2010 as applied in forestry and the indirect H2 index as a possible alternative. https://doi.org/10.1007/s11192-012-0618-8

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

KEEP EXPLORING

Related papers

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

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

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