L’auto-ethnographie collaborative organisationnelle (ACOR) : quand le « je » devient « nous »
Bibliographic record
Abstract
L’objectif de cet article est d’offrir à la communauté scientifique une méthode émergente pour la recherche dans les organisations, soit l’auto-ethnographie collaborative organisationnelle (ACOR). Bien que l’auto-ethnographie soit pratiquée depuis quelques dizaines d’années, sa version collaborative est plus rare et n’a été que très peu utilisée en recherche organisationnelle. L’article fait ressortir les avantages de cette méthode sur l’ethnographie et l’auto-ethnographie individuelle, particulièrement pour ce qui est de la rigueur scientifique, de l’introspection, de la rétrospection et de l’éthique. L’article présente aussi les étapes de base de l’ACOR en s’appuyant sur une étude qui a été effectuée auprès d’une équipe de vente.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".