La rencontre humaine est-elle soluble dans l’intelligence artificielle ?
Bibliographic record
Abstract
Avec la numérisation du monde, la réalité n’est plus ce qu’elle était. Je peux avoir l’illusion d’être ici et ailleurs. Un cloud remplace ma mémoire personnelle. L’autre du débat s’efface au profit du même des communautés virtuelles. La 4e révolution industrielle n’est pas qu’un saut technologique, c’est surtout un choix de société qui renouvelle en profondeur l’exercice du leadership et ses trois fondements humanistes : la fragilité, l’altérité et la responsabilité. L’irruption d’outils de machine learning tels que Chat-GPT transforme violemment les métiers de la prestation intellectuelle. Un algorithme sophistiqué peut désormais produire un langage cohérent, vraisemblable et interactif. A l’image des avocats pressés, des recruteurs en batterie et des collégiens paresseux, la tribu coach voit muter sa liturgie de la présence. Concrètement, quelle place l’intelligence artificielle (IA) va-t-elle prendre dans l’accompagnement humain ?
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".