Bibliographic record
Abstract
In this paper, we develop the concept of "negative identity" to enlighten the identity work of auditors in major firms. It refers to the tension by which individuals create their professional identity through a continuous and fragile rapport with their difficulties and own weaknesses. To do so, individuals strive to create a coherence between their personal identity and their social identity, i.e. between their intrinsic qualities (and weaknesses) and the professional roles expected from them in the firm. Based on a six month ethnographic study, we illustrate the mechanics of auditor's negative identity around three core sets of practices and discourses: experimenting, confessing and administrating difficulties and weaknesses. By tracing these sets of practices, we investigate a lesser known aspect of auditor's work, and show that the difficult rapport that an individual can have with his own weaknesses might not be an undesirable effect of the job, but one of its main drivers.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".