Bibliographic record
Abstract
Dans cet article, nous développons la conception d’une identité « négative » de l’auditeur pour comprendre le travail identitaire dans les cabinets du Big Four. L’identité négative ne sert pas à la critique d’un métier. Elle décrit l’ensemble des pratiques et des discours par lesquels le praticien se construit en « bon professionnel » dans un rapport pressant et continu à ses faiblesses. Au moyen d’une étude ethnographique de six mois, nous montrons que l’identité négative se structure autour de l’expérience, de la confession, et de l’administration de ses faiblesses par l’auditeur. Ces trois ensembles de pratiques et de discours permettent de comprendre une partie méconnue du travail identitaire de l’auditeur et d’analyser comment les difficultés qu’il rencontre sur le terrain, participent à la mise en cohérence de son identité personnelle et de son identité sociale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".