Knowledge Hierarchies and Gender Disparities in Social Science Funding
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it