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Record W2136666516 · doi:10.60082/2817-5069.1032

A Model of Responsive Workplace Law

2012· article· en· W2136666516 on OpenAlexaffvenue
David J. Doorey

Bibliographic record

VenueOsgoode Hall law journal · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsYork University
Fundersnot available
KeywordsCollective bargainingLabour lawScholarshipPoliticsNegotiationLaw and economicsLawSociologyEconomicsPolitical science

Abstract

fetched live from OpenAlex

The North American model of workplace law is broken, characterized by declining frequency of collective bargaining, high levels of non-compliance with employment regulation, and political deadlock. This paper explores whether the theory of “decentred regulation” offers useful insights into the challenge of improving compliance with employment standards laws. It argues that the dominant political perspective on workplace regulation today is managerialist. Politicians with a managerialist orientation reject both the pluralist idea that collective bargaining is always preferred and the neoclassical view that it never is. Managerialists accept a role for employment regulation and unions, particularly in dealing with recalcitrant employers who mistreat their employees. The fact that managerialists and pluralists agree on this latter point creates a space for potential movement on workplace law reform. A law that encourages “high road” employment practices, while fast-tracking access to collective bargaining for “low road” employers could encourage greater compliance with employment regulation, while also facilitating collective bargaining at high-risk workplaces. This article examines lessons from scholarship on decentred regulation for the design of a legal model capable of achieving these results. In particular, it develops and assesses a dual regulatory stream model that restricts existing rights of employers to resist their employees’ efforts to unionize once they have been found in violation of targeted employment regulation.

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 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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.312
Teacher spread0.270 · 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 teacher head, 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

Citations5
Published2012
Admission routes2
Has abstractyes

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