Working Together in Montréal to Improve Veterans’ Well-Being: A Canadian Perspective
Bibliographic record
Abstract
Services for veterans in Canada can be unclear and difficult to navigate for civilian service providers working with veterans. In this article, we feature two Montréal-based initiatives that aim to improve services for veterans through collaboration, the Old Brewery Mission and Respect Forum. We begin by providing background information about Canada’s recent history of military engagements and veterans affairs issues. The first example of collaboration presented is the Sentinelles de la rue (Sentinels of the Street) program, led by the Old Brewery Mission. The Mission works with Montréal’s homeless men and women, meeting their essential needs while finding practical and sustainable solutions to end chronic homelessness. The Mission is now developing a collaborative model in partnership with government departments, veterans peer support organizations, and local health and social services to house and support homeless military veterans. The second example is Respect Forum, a not-for-profit initiative that has been organizing networking events in Montréal, Québec since 2016. The aim of these events is to promote military–civilian and multisectoral collaboration to improve services for veterans. Respect Forum meetings have made it possible to begin bringing together and mapping out local and national service providers working with veterans.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.042 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".