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Record W2754346920 · doi:10.12927/hcpap.2017.25200

Enhancing the Relationship Between Regulators and Their Profession

2017· article· en· W2754346920 on OpenAlexaffvenue
Zubin Austin

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccountabilityFace (sociological concept)Balance (ability)Work (physics)Public relationssortPolitical sciencePublic administrationPsychologySociologyLawEngineeringSocial scienceComputer science

Abstract

fetched live from OpenAlex

Regulators face unique pressures to balance competing priorities related to patient safety, public accountability, and practitioners' expectations. Historically, the collegial model of self-regulation has been used as a tool for risk management, to recognize the importance of profession- and context-specific judgment in complex, ambiguous clinical situations. Increasingly, as public accountability concerns have grown dominant within regulatory bodies, this collegial model has shifted toward a more antagonistic relationship between the regulators and the regulated. Wilkie and Tzountzouris (2017) highlight one profession's journey toward embedding professionalism within regulatory practices and policies through application of a right-touch regulatory philosophy. Given the complexity of regulatory work, this shift required significant strategic and deliberative thinking. The challenges of facilitating this sort of cultural shift in the role of a regulator are significant, but so too are the potential gains associated with a more engaged relationship between regulators and their practitioners.

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 imitation

Not 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.

metaresearch head score (Codex)0.120
metaresearch head score (Gemma)0.188
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.120
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.188
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0310.025
Scholarly communication0.0350.029
Open science0.0040.036
Research integrity0.0160.025
Insufficient payload (model declined to judge)0.0220.008

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.

Opus teacher head0.537
GPT teacher head0.557
Teacher spread0.021 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations3
Published2017
Admission routes2
Has abstractyes

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