Enseignement supérieur : mythes et réalités de la révolution digitale
Bibliographic record
Abstract
Dans cet article, à partir d’une revue de la littérature et de données empiriques collectées au cours d’entretiens semi-directifs, nous proposerons une analyse de l’influence de la digitalisation sur l’enseignement supérieur. Nous porterons plus particulièrement notre l’attention sur l’évolution de la façon d’enseigner dans les écoles de management et sur les relations existant entre les organisations, les enseignants et les étudiants. Nous questionnerons la légitimité des organisations – les écoles de management – face à ce nouveau défi qui pourrait n’être qu’une mode. Dans la discussion nous évoquerons plus particulièrement trois points : a) l’effet de mimétisme des organisations face au digital ; b) la nouvelle quête de légitimité des organisations ; et c) l’évolution potentielle du Business model des organisations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".