Unblurring the Lines of Responsibility: The Puzzle of Veteran Service Provision and its Gendered Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it