Comment « donner forme » à des connaissances sensorielles en contexte de formation? Analyse des interactions lors d’une séance de formation en entreprise
Bibliographic record
Abstract
Notre objectif est de souligner en quoi la matérialité, la corporéité et le langage jouent un rôle clé dans la constitution de connaissances sensorielles en contexte organisationnel. Plus précisément, nous sommes intéressée par les processus communicationnels soutenant la constitution de connaissances sensorielles. La « mise en forme » de connaissances sensorielles soulève des questions particulièrement pertinentes pour les chercheurs en communication, car les expériences sensorielles en situation de travail sont difficiles à formaliser et à transmettre. Notre étude se concentre sur l’analyse de ressources multimodales (discursives, matérielles et corporelles) à travers lesquelles des connaissances sensorielles sont générées, façonnées et partagées en contexte de formation professionnelle. Notre analyse est basée sur une analyse multimodale de l’interaction et la situation empirique analysée dans ce texte est une session de formation entre une consultante en hydrogéologie et une stagiaire.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".