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Record W6903540210 · doi:10.1093/scipol/scaf012

Gender differences in Australian research grant awards, applications, amounts, and workforce participation

2025· article· en· W6903540210 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience and Public Policy · 2025
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsKensington Health
FundersNational Health and Medical Research CouncilUniversity of New South WalesAustralian Government
KeywordsWorkforceGrant fundingWorkforce planningPublic policyPublic funding

Abstract

fetched live from OpenAlex

Abstract We modelled two decades (2000–20) of Australian national competitive grants according to lead investigator gender. We also explored whether gender differences in awarded grants mirrored application rates and/or research workforce participation by gender. We found that fewer awarded grants were led by women than men; however, overall success rates of grant applications did not vary according to lead investigator gender. There were fewer women than men in the research workforce. The award rate (awarded grants relative to workforce participation) was slightly higher for women than men. Most of these observed gender differences were largest at senior-career levels. Together, these patterns imply that fewer women in the research workforce and leading grant applications have resulted in fewer awarded grants led by women than by men. We offer public policy measures to address women’s retention and progression in the research workforce.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0680.258
Science and technology studies0.0010.002
Scholarly communication0.0050.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.751
GPT teacher head0.642
Teacher spread0.109 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it