Prestige at Play: University Hierarchies and the Reproduction of Funding Inequalities
Bibliographic record
Abstract
This article examines the relationship between university prestige, disciplinary cultures, and the (re)production of funding inequalities in the humanities and social sciences. We combine qualitative and quantitative methods by analyzing: (1) data on 56,680 successful and unsuccessful grant applications submitted to the Canadian Social Sciences and Humanities Research Council; (2) 43 interviews with past members of review committees, including in economics, history, sociology, and political science. Our findings show that university affiliations significantly influence funding allocation: even after controlling for other factors, scholars at more prestigious and larger institutions are more likely to secure grants for greater amounts. For the Insight grants, applicants affiliated with U3 universities receive, on average, nearly 20,000$ more than their colleagues from institutions outside the U15. This effect is strongest in disciplines where scientific quality is clearly defined and tightly linked to institutional status. In contrast, in disciplines where the definition of merit is more ambiguous and debated, evaluators rely less on university affiliation, and prestige plays a diminished role. These divergences highlight the need to distinguish between the formal, general norms adopted by funding agencies and the unwritten, situated norms that review committees rely on to evaluate and rank applications within their respective fields.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".