Les pratiques pédagogiques « exemplaires » en sciences de l’information
Bibliographic record
Abstract
L’article s’intéresse aux pratiques pédagogiques « exemplaires » en sciences de l’information. Après une mise en contexte et quelques précisions d’ordre méthodologique, nous présentons une recension des écrits consacrés à ce sujet en trois volets : des réflexions théoriques sur les pratiques pédagogiques en sciences de l’information, des guides proposant des approches d’enseignement, et des bilans d’expérience rapportés par des enseignants. Nous dressons ensuite l’inventaire des pratiques pédagogiques « exemplaires » identifiées par les enseignants de l’École de bibliothéconomie et des sciences de l’information (EBSI). Enfin, nous tirons quelques constats à partir de la littérature recensée et de l’inventaire effectué.
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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 | 0.026 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".