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Record W4308967766 · doi:10.7577/pp.4962

What is the Public Interest in Professional Regulation? Canadian Regulatory Leaders’ Views in a Context of Change

2022· article· en· W4308967766 on OpenAlexaffabout
Tracey L. Adams

Bibliographic record

VenueProfessions and Professionalism · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsRationalization (economics)Public interestRationalityPublic relationsContext (archaeology)SociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Professions are regulated in the public interest, but precisely what the term “public interest” means can vary across time and place. Research exploring changes to professional regulation in the West has begun to identify such shifts: for instance, highlighting the emphasis on consumer satisfaction and public protection over other potential meanings of the public interest. To understand these societal shifts and their implications for professional regulation, this article first reviews neo-Weberian theories of rationalization, and empirical literature. Subsequently, it presents findings from interviews with regulatory leaders across six Canadian provinces to determine if the trends in rationalization identified are reflected in leaders’ accounts of professional regulation in the public interest. Interviews reveal that many leaders define the public interest in ways consistent with technical rationality, including a safety lens and consumer orientation; however, there is also evidence of broader meanings and values. The implications of these findings are discussed.

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)0.016
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0360.043
Scholarly communication0.0180.004
Open science0.0020.003
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.288
GPT teacher head0.345
Teacher spread0.056 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2022
Admission routes2
Has abstractyes

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