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Record W3203485884 · doi:10.1177/00207020211050330

How emerging trends in historiography expose the Canadian Army’s past discriminatory practices and provide hope for future change

2021· article· en· W3203485884 on OpenAlexaboutno aff
Isabel Campbell

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyGender studiesHuman sexualitySociologyRace (biology)White (mutation)CriminologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

This “lessons learned” article examines how emerging trends over time in the historiography of the Canadian Army have challenged and continue to challenge the white Anglophone masculine heterosexual culture which is especially associated with its combat units. This study began as an examination of the intersection between the historiography and the current priorities for sufficient female participation in the Canadian Armed Forces (CAF) which are intended to improve past abusive patriarchal practices and create effective and safe international interventions. Gender and sexual abuses were the initial foci, but the historiography revealed the interconnectedness of widespread discriminations against all “others”—defined here as anyone with a different gender, sexuality, race, language, religion, or culture. The article opens with a brief summary of evolving feminist ideas about security forces in general. It then delves into the historiographical trends which have demonstrated how systemic discriminations have privileged white Anglo men in combat roles while underplaying their contributions and the contributions of “others” in support roles in the Canadian Army over time. The key lesson learned from this work is that gender balance alone is not enough to address the profound cultural issues which plague the Canadian Army.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.039
GPT teacher head0.345
Teacher spread0.306 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2021
Admission routes1
Has abstractyes

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