Ethnographie et théorie de la description – La construction des données sociologiques
Bibliographic record
Abstract
Peut-on concilier l’approche ethnographique et les visées scientifiques en sciences sociales ? À partir d’exemples de travail de description et de réflexions méthodologiques que l’on peut en tirer, nous voulons montrer la nécessité de reconsidérer la conceptualisation des phases de la démarche ethnographique allant de l’heuristique de la découverte à la construction d’un objet de connaissance transmissible explicitement. Cet exercice de conceptualisation de certains aspects du travail ethnographique permettra d’avancer la proposition suivante : le projet d’une théorie de la description du social dans ses formulations anciennes ou plus récentes, loin d’être un carcan pour l’approche ethnographique, serait une voie privilégiée pour inscrire cette démarche dans une visée scientifique. Plus encore, l’approche ethnographique pourrait bien être une contribution significative aux sciences sociales dont la scientificité demeure problématique.
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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 | 0.047 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".