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Record W4200314248 · doi:10.1111/ilr.12345

Labour law in the 100 years of the <i>International Labour Review</i>

2021· article· en· W4200314248 on OpenAlexaff
Ruth Dukes, Judy Fudge, Guy Mundlak

Bibliographic record

VenueInternational Labour Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Labor and Employment Law
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLabour lawScope (computer science)NarrativeWeavingSelection (genetic algorithm)LawChild labourSociologyInternational lawPolitical scienceLaw and economicsWork (physics)LinguisticsEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract This Centenary Issue presents a selection of the articles on labour law that were published in the International Labour Review (ILR) during its first 100 years. The articles were chosen for their contribution to two tracks of debate and discourse. The first concerns a progression of ideas over time with respect to the scope and substance of labour law in general. The second charts the evolution of international labour law more specifically. This introduction provides a narrative weaving together the landmarks that were selected in each of these tracks and pointing out recurring themes, questions and challenges as well as changing points of view.

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)0.022
metaresearch head score (Gemma)0.050
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.018
Science and technology studies0.0030.006
Scholarly communication0.0180.011
Open science0.0010.004
Research integrity0.0040.007
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 head0.027
GPT teacher head0.344
Teacher spread0.316 · 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
Published2021
Admission routes1
Has abstractyes

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