“Even if I Don't Know What I'm Doing I Can Make It Look like I Know What I'm Doing”: Becoming a Doctor in the 1990s*
Bibliographic record
Abstract
Les processus de socialisation des médecins documentés dans Boys in White et d'autres textes classiques n'ont guère changé, 40 ans plus tard, malgré une population étudiante manifestement plus variée. La plus grande différence se trouve dans la façon dont les étudiants d'aujourdhui intègrent leur identité professionnelle naissante au moi établi avant l'école de médecine. L'identité professionnelle pourrait moins bien correspondre au moi quand les étudiants sont des femmes, plus âgés, de la classe ouvrière, homosexuels ou de groupes minoritaires. Pourtant, ces étudiants pourraient posséder une certaine capacité de résistance à la socialisation professionnelle. The processes of medical professional socialization documented in Boys in White and other classics remain remarkably unchanged 40 years later, despite a markedly more diverse student population. The greatest difference lies in how students today integrate their emerging professional identities with the selves they were before medical school. The professional identity may “fit” less easily when students are women, older, working‐class, gay or lesbian, or from visible minority groups. Yet these students may also enjoy a particular ability to resist professional socialization.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".