La confection d’un guide d’entretien pas à pas dans l’enquête qualitative
Bibliographic record
Abstract
L’entretien, une méthode des plus importantes dans les enquêtes en sciences humaines et sociales, exige de la part des personnes chercheuses non seulement de bonnes compétences scientifiques et relationnelles, mais aussi, ce qui est parfois négligé, un bon degré de préparation. Le présent texte entend répondre à cette dernière lacune, d’une part en mettant de l’avant quelques propositions d’ordre épistémologique susceptibles de bien orienter la préparation ainsi que la conduite de l’entretien, et d’autre part en examinant en détail, pas à pas, le travail de confection du guide d’entretien en six étapes.
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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 | 0.147 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".