Bibliographic record
Abstract
Health professions regulators charged with the role of public protection are challenged to balance their mandate with appropriate policy interventions, particularly within a self-regulatory model. Applied strategically, emerging methods and trends in health systems and health professions regulation can inform regulatory practices in keeping with the regulator's role of reducing harm to the public. This requires a shift in thinking from a focus on how (i.e., resources and tools), to a focus on what, including clear problem identification, intended risks to be mitigated and consideration of outcomes and measurability of impact at the outset. Regulators should be enablers, not barriers to system change, but it is not their place to take on all of the challenges associated with monitoring and implementing interventions in response to health system evolution. Instead regulators must know their role, be willing to collaborate with other system players and work to implement regulatory interventions that complement rather than duplicate those better carried out by others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.138 | 0.376 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".