Two strategies used by local intersectoral networks to create healthier environments: a cross-case analysis in the Montreal urban setting
Bibliographic record
Abstract
Action aimed at developing healthier living conditions requires intersectoral collaboration, both across sectors and between levels of government. It also calls for the commitment of political and institutional authorities at municipal and higher levels. This article focuses on strategies of local intersectoral networks rooted in civil society for addressing living conditions. A longitudinal cross-case analysis was performed on eight case studies in Montreal (Canada). Data sources include 1445 documents and 41 interviews. Case studies were analyzed based on a theoretical framework focusing on critical events and a repertoire of transitional outcomes (TOs) that local intersectoral networks mobilised in order to produce change. The analysis focussed on the distribution of TOs in each case. Two types of strategies were identified. The Do It strategy relied primarily on acquiring resources as well as expanding and strengthening networks and projects. In this strategy, networks held the key decision-making and action levers to drive projects by themselves. In contrast, the Make It Happen strategy was mainly constructed around actions that led to self-representation and influencing others. In this strategy, networks held certain levers – such as mobilizing their citizen and community bases – but they also had to convince decision-makers to support action. This article describes and compares the key features of these two types of action strategies for local change.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".