Revitalização urbana integrada em Montreal: lições da iniciativa de desenvolvimento local
Bibliographic record
Abstract
This paper reflects upon an integrated territorial initiative known as RUI (Integrated Urban Revitalization), which has been implemented in a number of Montreal neighbourhoods that have been particularly afflicted by poverty and social exclusion. In these areas, a series of Integrated Urban Revitalization pilot-projects have been implemented with a view to creating new development opportunities. The inter-sectoral RUI approach (health, education, crime prevention, housing, employment, transportation, environment...) advances governance, gives rise to a new generation of networks and partnerships and exposes structures to participative democracy. The RUI initiative also purports to contribute to rebuilding the identity and culture of disadvantaged people, strengthening territorial bonds (the neighbourhood as a place of belonging and social interaction) and reinforcing territorial cohesion. The RUIincorporates a bottom-up logic and constitutes a stimulus to the social economy by reinforcing the networks and institutional capacity of Montreal’s society and economy, but the State cannot afford not to participate in the quest for a fairer and more sustainable urban development model. Some of the lessons from this experience can be usefully transferred and adapted to other territories subject to exclusion and poverty
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".