Gender Diversity and Inclusion in Canadian Security Studies
Bibliographic record
Abstract
ABSTRACT Although much research confirms a gender gap in political science and its subfields internationally, only recently have scholars analyzed country-specific conditions for women within the field. Our study contributes to this national-level examination of gender diversity and inclusion by examining the extent to which a gender gap within the subfield of security studies, identified in the international literature, also is present in Canada. Research on gender representation and gendered experiences mostly centers on the academic workforce in the United States. However, in this article, we share the results of a multi-method investigation into the state of gender diversity in Canadian security studies—a national context in which the university sector has signaled a strong commitment to diversity and the government has actively promoted gender equality in official policy. By analyzing data collected from an online survey of security studies scholars in Canada and a document analysis of Canadian security-related journals and selected security studies syllabi, this contribution provides evidence that women are underrepresented in Canadian security studies and experience the subfield in less positive ways. We discuss the implications of these findings for the security studies subfield and suggest paths for future research and key recommendations.
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.
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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.028 |
| Science and technology studies | 0.042 | 0.014 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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".