Bibliographic record
Abstract
A number of actors are involved in the governance of homelessness in Montreal.Though there are some deep divides, there is also extensive coordination and collaboration.The Province of Quebec has a long history of involvement in homelessness, including with an interministerial plan on homelessness since 2010 and a policy on homelessness since 2014. 1 Provincial officials are also involved in the administration of non-Indigenous federal NHI/HPS funds in Quebec, though community plans are developed at the regional level by civil society actors.Following funding changes imposed by the federal government in 2014, which required 65 per cent of HPS funding to be allocated to Housing First programs, an additional regional plan, based on the province's plan, was created for Montreal.This regional plan prioritizes non-Housing First programs.The City of Montreal has become increasingly involved in homelessness governance in recent years as well.Montreal's first formal homelessness plan was introduced in 2010.A new plan, introduced in 2013, broke away from provincial priorities and advanced a distinct municipal vision.A 2018 municipal plan has continued to assert the city's viewpoint and increased its involvement, while also moving toward alignment with future provincial plans in terms of timing and approach.Third-sector groups have an institutionalized role in policy-making in Quebec, affording them greater legitimacy and influence than what is seen in other cities and provinces.One of the most important local third-sector actors is the Réseau d'aide au personnes seules et itinérantes de Montréal (RAPSIM), a group that advocates on behalf of people who experience homelessness as well as for service providers.It has existed since the 1970s.The RAPSIM does not have a formal plan on homelessness, but it is influential in policy-making across all three levels of government and in advocating for its preferred solution to homelessness: social
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.551 | 0.124 |
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".