La consolidation pragmatique et très élaborée de la loi québécoise sur la protection de la jeunesse
Bibliographic record
Abstract
In 2005-2006, almost simultaneously in Quebec and in France, there was a need to reform the legislation on child and youth protection, based on similar principles. This article presents the new elements and the perspectives of the Quebec legislation (Youth Protection Act amended by Bill 125 voted on 15 June 2006), in particular, contrasting it with the French legislation to reform child protection (5 March 2007). This article sheds light on significant differences. The development of the Quebec law reflects a true collaborative effort among the actors involved, in particular from civil society. It reveals the desire not to oppose a textual vision of the law to the practice of professionals. In addition, it strives to make the new provisions clear and precise, thus limiting their interpretation. This is particularly clear regarding the evaluation of the individual situations of children to be protected. Lastly, it shows the simple and widely disseminated tools used to support the implementation of the Quebec law. Since then, in terms of content, dissemination and method of development, the Quebec law has emerged as a reference for the development of the French legal mechanism in the medium term. This strengthens the importance of provisions relating to evaluation which, from then on, have appeared in the Quebec law on youth protection.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".