Producing law and governance: Disciplinary practices in the Colleges for Doctors and Nurses
Bibliographic record
Abstract
Professional self-regulating bodies define the boundaries of their occupational group membership, and the standards by which practitioners must abide to remain in that membership. This includes developing and employing discipline mechanisms through which professionals are surveilled and sanctioned. These mechanisms shape how we experience expert services, dictate which issues of professional conduct warrant correction and punishment, and wield the power to strip or curtail the livelihood of practitioners who have invested considerable time, money, and identity in obtaining the credentials and experience necessary to their trade. Despite the consequential scope there are, aside from lawyers, few studies of the formal discipline processes of self-regulating professions. Using a comprehensive data set of all discipline records from 1992 through 2017, as well as two years of in-person discipline tribunal observation, I trace the discipline processes of the College of Physicians and Surgeons of Ontario (CPSO) and the College of Nurses of Ontario (CNO) to examine how the boundaries, practices, and criteria of discipline compose the health professions and governance of doctors and nurses. I find, first, that the CPSO and CNO enact discipline in ways that serve the priority of maintaining self-regulation. Specifically, I show that patterns of allegations and punishments are imbued with practices that expand the scope of the Colleges' jurisdictions, enable discretion that obscures the severity of conduct, and solicit resources and compliance from their memberships. Further, I show that the Colleges prioritize tribunal performances of "governability" to prove their capacity to control their members. Second, I find that that the historical regulatory trajectories, regarding labour autonomy and status and professionalization, result in distinct experiences and outcomes of discipline for doctors and nurses that persist in the contemporary self-regulatory arrangement (despite ostensibly being standardized by the Regulated Health Professions Act).
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".