Getting Leopards to Change Their Spots: Co-Creating a New Professional Role Identity
Bibliographic record
Abstract
We investigated how professional role identity change can be accomplished in highly institutionalized contexts characterized by resiliency. We show that the collective professional role identity of family physicians was changed through a process of reinterpreting multiple logics and their relationships. Through our inductive analyses, we identified four mechanisms that occurred through social interactions and collectively served to rearrange the constellation of logics guiding physician role identity: (1) revealing the influence of a hidden logic; (2) reinforcing the conflict between logics; (3) reframing the meaning of a dominant logic; and (4) re-embedding the new arrangement of logics. We found that the change in physician professional role identity required significant identity work by a group of actors, but particularly by the managers who had been charged with leading the reform initiative. We contribute to the professional role identity and institutional literatures by showing how others can engage in social interactions with professionals to facilitate the reinterpretation and rearranging of institutional logics that guide collective professional role identity.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".