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Record W3126043811 · doi:10.1080/20414005.2015.1092267

The interactive dynamics of transnational business governance: A challenge for transnational legal theory

2015· article· en· W3126043811 on OpenAlexaff
Stepan Wood, Kenneth W. Abbott, Julia Black, Burkard Eberlein, Errol Meidinger

Bibliographic record

VenueTransnational Legal Theory · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsYork University
Fundersnot available
KeywordsTransnational governanceCorporate governanceScholarshipAgency (philosophy)Political scienceConvergence (economics)Dynamics (music)SociologyLaw and economicsPositive economicsSocial scienceEconomicsLawManagement

Abstract

fetched live from OpenAlex

Conflict, convergence, cooperation and competition among governance actors and institutions have long fascinated scholars of transnational law, yet transnational legal theorists' accounts of such interactions are for the most part tentative, incomplete and unsystematic. Having elsewhere proposed an overarching conceptual framework for the study of transnational business governance interactions (TBGI), in this article we propose criteria for middle-range theory-building. We argue that a portfolio of theoretical perspectives on transnational governance interactions should account for the multiplicity of interacting entities and scales of interaction; the co-evolution of social agency and structure; the multiple components of regulatory governance; the role of interactions as both influence and outcome; the diverse modes of interaction; the mechanisms and pathways of interaction; and the spatio-temporal dynamics of interaction. To suggest the value of these criteria, we apply them in a preliminary way to selected transnational legal scholarship and to the other articles in this special issue.

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)0.008
metaresearch head score (Gemma)0.008
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0050.049
Scholarly communication0.0140.021
Open science0.0030.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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
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

Citations42
Published2015
Admission routes1
Has abstractyes

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