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Record W4406624881 · doi:10.1017/s1049096524000271

Gender Diversity and Inclusion in Canadian Security Studies

2025· article· en· W4406624881 on OpenAlexafffundabout
Constance Duncombe, Stéfanie von Hlatky, Fernando G. Nuñez-Mietz, Maria Rost Rublee, Stephen M. Saideman

Bibliographic record

VenuePS Political Science & Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsCarleton UniversityMcGill UniversityQueen's University
FundersMinistère de la Défense Nationale
KeywordsDiversity (politics)Political scienceInclusion (mineral)Gender diversityGender studiesSociologyLawCorporate governanceManagementEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0100.004
Scholarly communication0.0000.000
Open science0.0010.008
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.376
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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