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Record W3112122969 · doi:10.1017/9781108781015.002

Explaining Public Interventions in Private Governance

2020· book-chapter· en· W3112122969 on OpenAlexaff
Stefan Renckens

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCorporate governanceBusinessPublic goodCredibilityPublic economicsContext (archaeology)Private goodEconomicsPolitical scienceFinanceMicroeconomicsLaw

Abstract

fetched live from OpenAlex

The chapter develops a theory of public intervention in private governance. It examines the conditions under which a public authority will intervene and the form this intervention will take: standards and/or procedural regulations or the absence of intervention. The chapter explains that the type of public intervention depends on the interplay of two variables: the domestic benefits of product differentiation and the fragmentation of the private governance market. On the one hand, a pubic authority may intervene in the market for sustainably certified goods to improve the competitive position of domestic producers, who are the main rule targets of private governance schemes. On the other hand, a public authority can intervene to structure a fragmented private governance market in order to overcome problems such as supply chain confusion, a lack of credibility of existing private governance schemes, and trade and competitive distortions. The chapter further conceptualizes private governance schemes as interest groups that engage in lobbying. It also explains the dynamics of the theory in the context of the EU policymaking process and how the interventions may evolve over time.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.019
Scholarly communication0.0060.007
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.072
GPT teacher head0.217
Teacher spread0.145 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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