Influencing practices of civil society-led intersectoral networks to improve living environments in the Montreal setting
Bibliographic record
Abstract
Population health and well-being are created through access to the necessary resources in living environments. Some of these can be addressed at the municipal level. Intersectoral action is seen as an appropriate strategy to face such challenges. Civic organizations involved in intersectoral coalitions can exert political influence upon municipal authorities to produce healthier living environments. This paper examines influencing practices of civil society-led intersectoral networks that would lead municipal authorities to effect such changes. Cross-case and longitudinal analyses were performed on six case studies of local intersectoral action in which the commitment of municipal authorities was necessary to bring about change. The study was based on a midrange theory on the process–effects links of local intersectoral action rooted in Actor-Network Theory. The results highlight two concrete practices deployed to influence public authorities: (1) intermediaries—which materialize convergent ideas and projects within networks—produced and placed in other networks, decision-makers, or media, and activated by them; (2) representations by spokespersons to communicate position, generate interest, or influence the position and commitment of strategic actors. Networks addressed actors in various social positions, from grassroots communities to municipal authorities and higher, institutions or private actors. They deployed and combined a wide variety of intermediaries and representations, both in collaborative and conflicting ways, to achieve change. Recognition of the relevance of these intermediaries and representations by the community and citizen bases on whose behalf the networks speak, and by the authorities they solicit, was a prerequisite in each case for progress leading to effects.
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How this classification was reachedexpand
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".