Émergence et mise en œuvre de l’approche de l’impact collectif pour l’inclusion sociale de personnes aînées au Québec: défis et facteurs facilitants
Bibliographic record
Abstract
The adoption of the collective impact approach in Quebec is relatively recent, and researchers are beginning to explore its specificities within the Quebec community sector. This article presents the findings of a case study that examines how community actors experienced collective impact through a regional initiative aimed at promoting the social inclusion of seniors. Data for the study were collected from 43 participants involved in the Initiative pour l’inclusion sociale de personnes aînées, un enjeu collectif through participatory observation, individual interviews, and focus groups. The findings highlight the obstacles and factors that facilitated the appropriation and implementation of the collective impact approach, thereby contributing to the scientific literature on the subject. Notably, the results revealed that collective impact can be perceived by community actors as “a top-down imposed model”. Its technical complexity and lack of popularization in Quebec were among the main factors that made its adoption difficult. The implementation of the approach within the Initiative faced several obstacles, such as the scope and diversity of the territory covered, the number and geographical distribution of the actors involved, as well as the health measures related to COVID-19.
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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 | 0.062 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".