L’amour peut-il rendre fou et autres questions scientifiques
Bibliographic record
Abstract
L'amour peut-il rendre fou ? La plupart des gens le croient, mais qu'en est-il vraiment ? Dominique Nancy et Mathieu-Robert Sauvé, tous deux journalistes à l'hebdomadaire Forum, ont eu la bonne idée de poser cette question, et une soixantaine d'autres d'inspirations diverses, à des experts de l'Université de Montréal, de HEC Montréal et de Polytechnique. Comment se forment les flocons de neige ? Les boissons énergisantes sont-elles bénéfiques ? Que se passerait-il si la Lune disparaissait ?... Questions loufoques, naïves ou angoissées. Leurs réponses, parfois étonnantes, mais toujours rigoureuses, ont d'abord été publiées dans le cadre des « Capsules science » du journal. Les voici toutes réunies, accompagnées d'une entrevue avec le philosophe Frédéric Bouchard pour qui « un résultat de recherche qui n'est pas partagé est une aberration ».
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".