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Record W652999896

Locating Labour Law: Conflicting Perspectives and the Case of Occupational Health and Safety

2006· article· en· W652999896 on OpenAlexaff
Eric Tucker

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsYork University
Fundersnot available
KeywordsLabour lawAssertionSalience (neuroscience)Context (archaeology)LawOccupational safety and healthPolitical scienceIndustrial relationsCommon lawHealth lawLaw and economicsSociologyHealth policyHealth carePsychologyInternational health
DOInot available

Abstract

fetched live from OpenAlex

While the need to locate employment and labour law in its social context is now widely recognized, there is significant disagreement over the character of that social context, how law is located in it, and the way that law both shapes and is shaped by its social location. The importance of these disputes is not just theoretical because their resolution shapes the way labour law is written and implemented. Nowhere is this truer than in one particular area of labour law, occupational health and safety (OHS) regulation. This chapter argues that from its origins in the nineteenth century, OHS regulation has been primarily guided by a consensus view of workers’ and employers’ interests in workplace health and safety. Historically, workers periodically resisted consensus-based regimes of regulation, insisting that the protection of their lives and health requires measures that recognize the salience of conflicts of interest and unequal power relations between workers and employers. From time to time workers succeeded in having the law reformed to address their concerns, but these accomplishments were undermined by the successful re-assertion of consensus perspectives in the implementation of regulatory reforms, resulting in regulatory failures that workers have paid for with their lives and health.

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: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.641

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 communication0.0000.000
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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