Unblurring the Lines of Responsibility: The Puzzle of Veteran Service Provision and its Gendered Implications
Bibliographic record
Abstract
The ever-increasing representation of women in the Canadian Armed Forces (CAF) has sparked discussion about the gendered implications of military-to-civilian transition. Women are now the fastest growing cohort of veterans in Canada and represent nearly 16% of the military. As the demographics of the military change in Canada and elsewhere, so too will the face of veterans. Despite the Government of Canada’s clear mandate to include gender-based analysis in all policies and programs, has this really been accomplished in the field of veteran service provision? We grapple with this challenge by problematizing the division of labour in veteran services and programs, examining whether programs have been responsive to gender mainstreaming commitments from the federal government. Finally, we demonstrate how a gender-based analysis can enhance services. We conduct a comprehensive environmental scan and create an original database for veteran services and programs in Ontario. A total of 211 individual programs and service offerings were examined and coded, with 5 found to integrate gender considerations into their program delivery. Our analysis is further supported by focus group data from 52 veterans. In addition to generating important recommendations for veteran service providers and employers tied to our data analysis, we also provide further best practices drawn from the experiences of two close allies, the United States, and Australia.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".