Typology of actors’ influence strategies in intersectoral governance process in Montreal, Canada
Bibliographic record
Abstract
The objective of this article is to document the strategies developed by actors from different sectors during the processes of intersectoral governance. As a case study, the article focuses on the process of renegotiating the Terms of Reference of the Montreal Initiative for Local Social Development, a regional intersectoral intervention with the mission of guiding and supporting the actions of local intersectoral coalitions referred to as Neighbourhood Round Tables. The renegotiation process was marked by crisis in intersectoral governance. Semi-structured individual interviews were conducted with 16 actors representing the four sectors involved in the intersectoral governance process, about four systematically selected critical incidents. The interviews were transcribed verbatim and coded using QDAMiner software. Interpretive and cross-sectional thematic analysis was conducted to assign meaning to the results. Results show that the actors developed intersectoral (shared or mediated) strategies and intrasectoral (creative unilateral, power-based unilateral or multilateral) strategies to influence collective decisions. The strategies, presented in a proposed typology, were distinguishable by their goals, their organizational origin, the actors involved and their fundamental mechanisms. Intersectoral strategies were developed at the regional level and aimed to promote or defend collective interests. In contrast, intrasectoral strategies sought to protect sectoral interests. The findings illustrate how actors' strategies operate within intersectoral governance processes. They show that collective decisions are shaped by the strategies created both at the boundaries of, and within, sectors. The proposed typology, if validated and applied to other cases, may help better understand how partners interact to influence collective decision-making.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".