Le protocole sociojudiciaire en matière de violence conjugale: Une mobilisation ciblée pour une innovation limitée dans un champ fortement conflictuel
Bibliographic record
Abstract
Cross-sector collaboration that leads to sustained systematic referral to police and social–legal support for women victims of conjugal violence continues to present a challenge in this field of intervention. This article discusses an evaluation of planning practices used for the development, implementation and sustainability of this type of innovative initiative: the socio-legal protocol in the region of Bas Saint-Laurent in Québec. The approach used was a longitudinal interpretive study based on a social theory of innovation, the theory of innovation translation. The planning practices deployed consisted in a strategic breakdown of the desired outcome and actors to mobilise, and the implementation of collaborative arrangements that made it possible to overcome controversies that have historically divided this field. The study shows that initial commitment of the various actors and demonstration of the feasibility of such an initiative are not enough to ensure it is sustained within organisations. These types of innovations rest on a patiently constructed network of alliances that is never stabilised. To facilitate continuity of these initiatives, their promoters must maintain a constant vigilance aimed at perpetually reinforcing the network of actors and preventing committed sectors from discontinuing their participation.
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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 | 0.313 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".