De l'intérêt d'articuler les dimensions discursive et interactionnelle de la conversation. Le cas d'une profession en voie de légitimation
Bibliographic record
Abstract
Cet article est consacré à deux dimensions bien connues de la conversation (au sens large du terme) : les dimensions discursive et interactionnelle. L’objectif est de montrer que leur articulation dans l’analyse de tout ensemble de données enrichit notre compréhension de ses enjeux en ce qui a trait à la question des rapports entre l’échelle locale de l’événement singulier de parole et l’échelle globale des patterns sociaux. L’analyse s’appuie sur des consultations sage-femme/cliente au Québec. Sur le plan interactionnel, la consultation se distingue peu des interactions médecin/patient. Sur le plan discursif cependant, les sages-femmes construisent une identité distincte de celle des médecins. Seule l’articulation des deux dimensions de ce que l’on peut observer à l’échelle micro du langage permet de dresser un portrait précis de ce qui se passe à l’échelle sociale macro.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".