Bibliographic record
Abstract
Compte rendu d'expérience Résumé Sans en reprendre la description détaillée, le présent article propose une analyse critique des options prises dans Form@sup, formation postmaîtrise d'une année pour les enseignants du supérieur désireux de s'interroger sur leur pratique professionnelle en développant leur cours en ligne.L'article justifie les fondements théoriques de ce dispositif et souligne les difficultés rencontrées dans la mise en œuvre de chacun de ses principes de base.Il présente ensuite les options prises pour 2005-2006, insistant sur l'influence du courant de professionnalisation de l'enseignement dans le supérieur.Par ses exemples concrets, il vise aussi à susciter la réflexion et l'échange de pratiques parmi les équipes de « développeurs instructionnels ».Summary Without resuming its detailed description, the present article proposes a critical analysis of the options taken in Form@sup, a one-year postgraduate degree for higher education teachers who want to question their professional practice through the development of their online course.The article justifies the theoretical backgrounds of this training program and underlines the difficulties encountered in carrying out each of its basic principles.It then presents the decisions made for 2005-2006, insisting on the influence of the Scholarship of Teaching and Learning movement.Through its concrete examples, it also aims at fostering reflection and exchange of practice amongst the instructional developers teams.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".