Bibliographic record
Abstract
— Quel est le numéro pour contacter un conseiller RH ? demande Paul au robot. — Je ne comprends pas votre question, répond le chatbot en souriant béatement. Cet aveu illustre la difficulté des dispositifs techniques « intelligents » à aider les salarié·es et à appréhender plus largement ce qui caractérise les situations de vie humaine. Mais alors, en quoi ces chatbots, développés en masse dans les entreprises, constituent-ils de nouvelles ressources pour les activités ? Comment transforment-ils le travail ? Pour y répondre, cet article propose un regard holistique sur le déploiement de chatbots dans un grand groupe français – l’Entreprise. L’enquête explore d’abord le contexte qui pousse aujourd’hui les industriels à innover à tout prix. Elle montre, dans un second temps, comment les salarié·es du groupe parviennent à s’approprier ces dispositifs techniques, d’une manière différente de celle prévue par les dirigeant.e.s et les concepteur·trices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 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".