Bibliographic record
Abstract
Ce texte est la transcription d'un exposé donné par le professeur Bonenfant le 26 septembre 1977, soit dix jours à peine avant son décès, à l'occasion d'un colloque international sur la rédaction législative. On peut donc le considérer comme son testament scientifique en ce qui concerne la légistique, discipline à laquelle il portait depuis plusieurs années un vif intérêt. Sa riche expérience des institutions parlementaires faisait de lui un observateur très écouté du récent renouvellement des techniques législatives au Québec. Prononcé à partir de simples notes, cet exposé conserve, tel que nous le publions, tout le jaillissement et le mordant qui caractérisaient le discours de notre collègue. Ceux qui l'ont côtoyé y retrouveront la chaleur, l'enthousiasme et la profonde sagesse qu'il apportait à la vie de notre Faculté. C'est donc aussi pour son intérêt humain que nous tenions à faire figurer ce texte dans ce recueil d'hommages. Il convient de remercier le Conseil de la langue française, organisateur de ce colloque, qui a assuré la transcription et la mise en forme de cette conférence, et nous a aimablement autorisés à le reproduire.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | low |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".