Bibliographic record
Abstract
In recent years, the practice of law has come under renewed criticism as to the objectives it pursues. Out of this debate grew the concept of preventive law. This paper seeks to explain the meaning of preventive law and demonstrate its relevance for the legal system of Québec, by outlining its main features, its peculiar methods and a strategy for its implementation. Preventive law can be most clearly distinguished from the traditional practice of law by a shift in priorities away from litigation to the maximization of certainty as to one's rights and duties. This new approach involves reform-mindedness, sensitivity to the citizen's needs and an offensive rather than defensive outlook. The typical preventive-law method is the annual check-up of the citizen's « legal health ». This requires the devising of checklists through which the safety of legal transactions may be ascertained. Other preventive-law methods include standard contract forms and legal self-aid kits. The implementation of a preventive-law approach should involve three centres of responsibility. The lawyer's office would of course remain the major stage on which the practice of law is carried out. But the focal point for initiating and developing preventive-law methods must be located elsewhere. In the Quebec context, the Société québécoise d'information juridique, being already active in the field of legal information, seems naturally suited to the task. In the short term, however, law schools must provide the initial impetus towards a policy of legal prevention.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.050 | 0.009 |
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".