L’élaboration de la norme législative et la prise en compte du savoir scientifique
Bibliographic record
Abstract
«La controverse autour du principe de précaution « versus principe d’innovation » est au cœur de différents débats législatifs (OGM, ondes électromagnétiques, nanomatériaux, risques chimiques, perturbateurs endocriniens…). Cet article propose des clés de lecture de la prise en compte du savoir scientifique dans l’élaboration de la norme législative à travers l’illustration du débat parlementaire relatif à la loi dite ondes du 9 février 2015 dont l’adoption fut quelque peu tumultueuse. Ce parcours législatif témoigne de deux occasions manquées pour le législateur : avoir fait preuve, d’une part, d’innovation dans la fabrique de la loi en appliquant le principe de précaution à un secteur marqué par la prégnance de l’incertitude scientifique et, d’autre part, d’audace en appliquant dans le même temps le principe ALARA (traditionnellement réservés aux rayonnements ionisants) aux ondes électromagnétiques (rayonnements non-ionisants). The controversy surrounding the precautionary principle “Versus the principle of Innovation” is at the center of different legislative debates (GMO, electromagnetic waves, nano materials, chemical risks, endocrine disruptors...). This article proposes reading tools on the basis of scientific knowledge to understand the elaboration of legislative norms illustrated by the parliamentary debate on electromagnetic waves on February 9, 2015, whose adoption was somewhat tumultuous. This legislative process reflects two missed opportunities for the legislator : to prove, firstly, the innovation in the construction of the law by applying the precautionary principle to a sector marked by the predominance of scientific uncertainty and, secondly, the boldness by applying at the same time the ALARA principle (traditionally reserved for ionizing radiation) to electromagnetic waves (non-ionizing radiation).
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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 | 0.068 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".