Diversifying theory and models for change: Why multiple frameworks better advance equity and belonging in the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: Critical theory as well as formal analytic models and tools across gender, sociological, and intersectional studies provide insight as to why generic culture change initiatives have not led to desired outcomes in the Department of National Defence (DND) and Canadian Armed Forces (CAF). Diversifying theories and models for culture change and applying these separately for DND and CAF may better increase cultural changes intended to advance equity and belonging. Methods: A review of critical theory is used to illustrate how social constructions of power relate to inequity across institutions, including the military. Moreover, institutional and social identity theory are presented to identify substantive differences between DND and CAF. An application of Richard Scott's method of institutional analysis highlights distinct organizational functions of DND and CAF, while the use of gender-based analysis plus identifies diversity in treatment and experience related to social hierarchies maintained uniquely within each organization. Finally, an analysis of knowledge transfer, legitimacy, power, and urgency of diverse groups illuminates historic social constructions of power, setting the conditions for contemporary inequities. Results: Findings illustrate that a one-size-fits-all approach to culture change across the wider Defence Team may avoid necessary thinking about institutional distinctions. Discussion: To fully comprehend inequities experienced by DND personnel and CAF members, each institution requires separate but comparative approaches to change. These benefit from applications of critical theory, as well as diversified analysis models and tools to address localized social constructions of power and inequity.
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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 | 0.040 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.023 | 0.110 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".