Méthodes de recherche en management
Bibliographic record
Abstract
Ce chapitre présente d’abord la collecte des données primaires. À ce titre, il décrit les techniques utilisables en recherche quantitative : questionnaire, observation et méthode expérimentale. Il expose ensuite les outils de collecte de la recherche qualitative : entretien individuel, entretien de groupe, observation participante et non participante. Il analyse alors la gestion des sources de données, en termes d’accès, de flexibilité du chercheur, de risques de contamination et de perte du chantier de recherche. Le chapitre recense quelques stratégies d’approche et de gestion des sources fondées sur le formalisme de la relation entre le chercheur et les individus-sources de données, sur le caractère dissimulé ou ouvert de l’investigation et sur le degré d’intimité à adopter à l’égard des sujets-sources. Le chapitre montre ensuite l’intérêt et les limites de la collecte des données secondaires internes et externes aux organisations. Enfin, il indique les conditions de préservation de la confidentialité de la recherche, et ses conséquences sur la validation des résultats par les sujets-sources et sur la publication de la recherche.
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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 | 0.074 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.018 |
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".