Situated Professionalism: Navigating Competing Institutional Logics of Professionalism and Market
Bibliographic record
Abstract
I develop a theory about situated professionalism by exploring how professional actors navigate the conflicting institutional logics of professionalism (with its emphasis on norms, ethics and reputation) and the market (which foregrounds self-interest and financial performance). Rather than being transitionary or short-lived, the presence of these two conflicting logics is enduring and shapes the day-to-day activity patterns of these professional actors by enabling them to draw on aspects of either logic depending on the situation. I propose and test a set of theoretical propositions highlighting that the accessibility and salience of the professional logic as a driver of professional action depends on environment- and task-related factors. Using a difference-in-difference empirical approach, I test the hypotheses on the large population of fee-for-service physicians in the province of Ontario, Canada over a four year period. In particular, I examine how these professionals’ activity patterns change in response to financial incentives. The results indicate that while professionals do draw from the market logic, the professional logic is more salient and accessible in two situations: (1) in larger organizations with characteristics – such as peer monitoring and routines – that reify the professional logic; and (2) in situations where a response to incentives is in greater potential conflict with professional norms.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".