Experts without Expertise: Repairing Professional Identity Through the Professional Motive
Bibliographic record
Abstract
Expertise, or “what we do,” is the foundation of professional identity, or “who we are.” We examine a case of extreme disruption to expertise where, in contrast to previous studies, professionals were unable to repair their identity through alternative expertise. At the onset of the AIDS epidemic in the US, doctors were confronted with a novel and intractable problem; for over fifteen years, physicians were at a loss for how to effectively treat and cure AIDS patients. We found that this prolonged inability to generate and apply expertise led many doctors to question the “meaning of medicine”— their professional identity. To repair their identity, physicians initially reconnected with the professional motive: the reason a profession exists. They then reinterpreted this motive and discovered new ways to enact their identity. They came to see client relationships as essential, and integrated them alongside expertise to constitute a multi-component professional identity. We contribute to theory on identity work by highlighting the professional motive as a foundation of identity and a key mechanism in this process. In considering clients, rather than professional peers or competitors, we also build on the burgeoning relational perspective in the professions literature.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.045 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| 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".