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Record W3213405808 · doi:10.1111/ilr.12339

Law and gendered labour market segmentation

2021· article· en· W3213405808 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Labour Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiscrimination and Equality Law
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScope (computer science)UniversalizationTransformative learningSituatedLabour lawMarket segmentationSociologyLabour economicsEconomicsLaw and economicsLawPolitical scienceMicroeconomicsEconomy

Abstract

fetched live from OpenAlex

Abstract This article captures the shared understanding in the literature of labour law's interaction with gender, distinguishing between law's different functions – constituting labour market institutions, sustaining them, addressing unwarranted outcomes and transforming the institutions. Constituted, in part, by law, the standard employment relationship and the institutions of formal employment have segmenting gendered effects. While legal norms designed to correct these effects are important, they also sustain them. The authors argue for a transformative alternative that would follow two general principles in designing new labour standards, namely, universalization of scope and adaptive content in the interests of differently situated women and men.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.992

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.053
GPT teacher head0.385
Teacher spread0.331 · 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