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Record W4409982604 · doi:10.3138/jmvfh-2024-0051

Diversifying theory and models for change: Why multiple frameworks better advance equity and belonging in the Canadian Armed Forces

2025· article· en· W4409982604 on OpenAlexaffvenueabout
Vanessa Brown

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsCanadian Forces College
Fundersnot available
KeywordsEquity (law)Political scienceBusinessEconomicsLaw

Abstract

fetched live from OpenAlex

LAY SUMMARY This article examines shortcomings of culture change initiatives within the Department of National Defence (DND) and Canadian Armed Forces (CAF). It argues that current one-size-fits-all approaches fail to address unique cultural dynamics in each institution. Using critical theory and various analytic models, the article highlights how social constructions of power contribute to persistent inequities in military and defence environments. By exploring militarized masculinities and intersectional, institutional, and social identity theories, it demonstrates differences between DND and CAF that need to be considered in culture change strategies. Additionally, analytic models such as Richard Scott’s institutional analysis reveal distinct organizational functions shaping everyday interactions within each entity, just as applications of the gender-based analysis plus (GBA Plus) tool uncovers inequitable experiences based on unique social hierarchies of DND versus CAF. Thus, effective culture change requires tailored approaches that recognize distinctions within DND and CAF. The article argues that to foster equity and belonging, diverse theoretical frameworks and tools are needed to address specific challenges faced by DND members and CAF personnel. A deeper understanding of these localized issues is essential for achieving desired cultural transformations.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.077
GPT teacher head0.367
Teacher spread0.290 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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