Granting Inequities: Racialization and Gender Differences in Social Science and Humanities Research Council of Canada's Grant Amounts for Research Elites
Bibliographic record
Abstract
Abstract: This paper explores inequities in the Social Science and Humanities Research Council of Canada (SSHRCC) grant amounts awarded to successful applicants in Canada's universities. Specifically, the study seeks to discern whether ethnicity and gender matter for amounts of SSHRCC's award decisions using compiled SSHRCC data and names of successful grant applicants to generate a large sample from 1998 to 2018. Although the importance of ethnicity along with gender and region, as well as institutional factors such as discipline and status of an applicant's university are explored in relation to higher education institutions' applicants, our focus is on effects of racialization on SSHRCC's decisions. We find that several factors affect the amount of grants awarded and that racialized applicants, especially men, have tended to receive less funding than non-racialized applicants over this period. In addition, on the one hand, the grant amount for racialized minorities has decreased in recent years. On the other hand, racialized women applicants received higher grant amounts than their counterparts, and particularly in recent years. Conceptual, empirical, methodological, and policy implications of these findings are discussed. Résumé: Cet article explore les inégalités dans les montants des subventions accordées par le Conseil de recherches en sciences humaines du Canada (CRSH) aux candidats retenus dans les universités canadiennes. Plus précisément, l'étude vise à déterminer si l'origine ethnique et le sexe ont une incidence sur le montant des subventions accordées par le CRSH, en utilisant les données compilées du CRSH et les noms des candidats retenus pour générer un vaste échantillon de 1998 à 2018. Bien que l'importance de l'ethnicité, du sexe et de la région, ainsi que des facteurs institutionnels tels que la discipline et le statut de l'université d'un candidat soient explorés par rapport aux candidats des établissements d'enseignement supérieur, nous centrons notre analyse sur les effets de la racialisation dans les décisions du CRSH. Nous postulons que plusieurs facteurs affectent le montant des subventions accordées et que les candidats racialisés, en particulier les hommes, ont tendance à recevoir moins de financement que les candidats non racialisés au cours de cette période. En outre, d'une part, le montant des subventions accordées aux minorités racialisées a diminué au cours des dernières années. D'autre part, les femmes racialisées candidates ont reçu un montant de subvention plus élevé que leurs homologues, en particulier au cours des dernières années. Les implications conceptuelles, empiriques, méthodologiques et politiques de ces résultats sont discutées.
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How this classification was reachedexpand
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 | 0.023 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".