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Record W2633196259 · doi:10.1017/aju.2017.15

The Rise of Sectorally Differentiated Contract Law

2017· article· en· W2633196259 on OpenAlexaff
Joshua Karton

Bibliographic record

VenueAJIL Unbound · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsQueen's University
Fundersnot available
KeywordsRulemakingPaceGlobalizationLawmakingProduct differentiationTreatyPhenomenonBusinessProduct (mathematics)State (computer science)Market economyInternational tradeLawEconomicsPolitical scienceLegislatureGeography

Abstract

fetched live from OpenAlex

This essay identifies an underappreciated side-effect of the increasing influence of industry associations in the development of transnational law. As the law governing commercial contracts harmonizes across territorial boundaries, it will increasingly split along boundaries between industry sectors, a phenomenon I call “sectoral differentiation.” Sectoral differentiation is largely a by-product of the growth of transnational legal orders in an environment where state laws and networks may be unable to keep pace with commercial globalization. Industry associations are not the sole drivers of sectoral differentiation, but their activities often promote it, either directly through rulemaking activities related to their particular industries, or indirectly through influence on treaty drafting and other national and international rulemaking processes.

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)0.014
metaresearch head score (Gemma)0.021
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: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.020
Scholarly communication0.0100.010
Open science0.0010.007
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.341
Teacher spread0.305 · 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
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
Published2017
Admission routes1
Has abstractyes

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