Le passé eugénique canadien et ses leçons au regard des nouvelles technologies génétiques
Bibliographic record
Abstract
L'eugénisme fascine. L'eugénisme fait peur. D'ailleurs, le spectre de cette idéologie, lié à un passé riche en histoire, revient en force et suscite des craintes de dérives quant à l'avancement des connaissances scientifiques et de leurs applications principalement dans le débat sur le diagnostic préimplantatoire. Alors que nous nous intéressons à son encadrement normatif, une approche historique permet de guider le législateur afin de ne pas répéter des erreurs passées et de mieux en comprendre les enjeux. Or, une loi eugénique a déjà existé au Canada en matière de stérilisation des personnes mentalement handicapées. À la lumière de celle-ci et de son histoire, l'auteur s'interroge sur les leçons que nous pouvons appliquer au diagnostic préimplantatoire, une technologie génétique permettant de sélectionner un enfant créé in vitro en fonction de son profil génétique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 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".