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Record W4389206942 · doi:10.22215/etd/2022-15800

Backtalk: Implementing United Nations Security Council Resolution 1325 in the Canadian Armed Forces

2022· dissertation· en· W4389206942 on OpenAlexfundaboutno aff
Victoria Tait

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsnot available
FundersMinistère de la Défense NationaleCanadian Armed Forces
KeywordsSecurity councilPolitical sciencePublic administrationNationalismDominance (genetics)PoliticsCivil societyMasculinityGovernment (linguistics)Political economySociologyLawGender studies

Abstract

fetched live from OpenAlex

This dissertation examines the implementation of United Nations Security Council Resolution 1325 in the Canadian Armed Forces (CAF). It argues that the gender equality norms contained within UNSCR 1325 have been distorted by the discursive framework used to achieve buy-in within the masculinized culture of the CAF. Fearing backlash prompted by unpopular past gender integration initiatives, a discourse of operational efficacy was used to introduce UNSCR 1325 to the Canadian military. This discourse functionally separated UNSCR 1325 from past and present initiatives designed to challenge the dominance of militarized masculinity in the CAF. The problematic culture of the CAF was further emboldened by a conservative political opportunity structure under the government of Prime Minister Stephen Harper, under whose governance the first Canadian National Action Plan on UNSCR 1325 (2010-2016) was drafted. The Harper administration advanced a reimagined Canadian nationalism that emphasized NATO engagement in Afghanistan at the expense of Canada’s liberal internationalist past, further pulling Canadian public sentiment and resources away from the United Nations.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.341
Teacher spread0.269 · 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.

Study designQualitative
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
Published2022
Admission routes2
Has abstractyes

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