Bibliographic record
Abstract
J'enseigne la linguistique au niveau collégial depuis 1971.Il m'a souvent été donné d'entendre des collègues de ma discipline, des spécialistes d'autres disciplines, des administrateurs scolaires, et les pre miers concernés, des étudiants, remettre en cause cet enseignement.Il n'y a donc rien d'étonnant à ce que je m'interroge encore sur quoi enseigner, et comment le faire.Bien sûr, ma réponse à ces questions a évolué avec l'expérience et la découverte de connaissances nouvelles.Néanmoins certains postulats implicites ont déterminé mon activité d'enseignant durant toutes ces années.J'aimerais commencer cette réflexion sur l'en seignement de la linguistique au collégial paruneexplicitation de ces postulats.Ils jetteront sans doute un éclairage utile à la compréhension de mes réponses actuelles aux questions que je pose ci-dessus. Quelques lieux ou postulats bien communsJ'ai toujours trouvé naturel, en tant qu'enseignant qui assume la préparation d'un cours, de prendre con-
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 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".