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Record W1769034834 · doi:10.1111/rego.12078

When doctors shape policy: The impact of self‐regulation on governing human biotechnology

2015· article· en· W1769034834 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRegulation & Governance · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversité de MontréalUniversity of Ottawa
Fundersnot available
KeywordsCorporate governanceIntervention (counseling)Field (mathematics)Mode (computer interface)BusinessBiotechnologyEconomic systemEconomicsBiologyFinanceMedicineComputer science

Abstract

fetched live from OpenAlex

Abstract This paper investigates the development and adoption of governance modes in the field of human biotechnology. As the field of human biotechnology is relatively new, voluntary professional self‐regulation constituted the initial governing mode. In the meantime, with the exception of I reland, all W estern E uropean countries have moved toward greater state intervention. Nevertheless, they have done so in contrasting ways and the resulting governance modes for assisted reproductive technology and embryonic stem‐cell research vary greatly. Instead of imposing their steering capacity in a “top‐down” fashion, governments have taken pre‐existing self‐regulatory arrangements in the field into account and built up governance mechanisms in conjunction with private actors and pre‐existing modes of private governance. Our analysis demonstrates that the form and content of the initial self‐regulation explain why the self‐steering capacity of the medical profession was largely or at least partially preserved through hybrid governance systems in B ritain and G ermany, while in F rance the self‐regulation was entirely replaced by governmental intervention.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.281
Teacher spread0.247 · 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