Knowledge Hierarchies and Gender Disparities in Social Science Funding
Bibliographic record
Abstract
This article examines the relationship between knowledge hierarchies and gender stratification in research funding. Through a mixed-methods study combining data on 5460 funded and unfunded social science applications submitted to a research council in Western Europe, and nine interviews with current and former council members, we explore how applicants’ disciplinary, thematic and methodological orientations intersect with gender to shape funding opportunities. Descriptive analysis indicates that women’s proposals are underfunded, with a relative gender difference of around 20%. Using computational text analysis and mediation analysis, we approximate that around one-third of this disparity may be attributed to gender differences in disciplinary focus, thematic specialisations and methodologies. The interviews with council members allow us to make sense of these disparities and expose the disciplinary hierarchies and power struggles at play in the council, sometimes resulting in a devaluation of qualitative methods and, more broadly, interpretive, descriptive and exploratory approaches in proposal assessments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".