Réaliser Une Recherche Ancrée Dans La Pratique Des Comptables Professionnels Grâce À L’entretien D’explicitation De L’action
Bibliographic record
Abstract
Une recente recension des ecrits effectuee par Mactavish et al. (2018) fait ressortir l’importance d’analyser divers facteurs qualitatifs qui influencent les auditeurs. Bien que le Manuel de CPA Canada fasse souvent reference a l’exercice du jugement professionnel, relativement peu d’ecrits abordent la facon de l’exercer en pratique. Comment recueillir des donnees concretes nous permettant d’identifier ces facteurs qualitatifs? Comment documenter le processus utilise par des professionnels experimentes qui font face a une situation empreinte d’incertitude? La methodologie de l’entretien d’explicitation de l’action, qui permet de conscientiser les experiences passees et de les documenter en les morcelant, nous est alors apparue comme toute indiquee pour repondre a ces questionnements. Cet article, a caractere methodologique, vise a decrire ce type d’entretien afin d’en faire ressortir les facteurs de succes, les avantages et les limites. Cette methode d’entretien, tres peu utilisee en sciences de la gestion, gagne a etre connue.
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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 | 0.068 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".