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Record W4366606391 · doi:10.1093/indlaw/dwad008

The Future of Unions and Worker Representation: The Digital Picket Line

2023· article· en· W4366606391 on OpenAlexaffabout
Valerio De Stefano

Bibliographic record

VenueIndustrial Law Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsYork University
Fundersnot available
KeywordsPicketingRepresentation (politics)PublishingPolitical scienceLine (geometry)Media studiesSociologyLawPolitics

Abstract

fetched live from OpenAlex

The Future of Unions and Worker Representation, written by Professor Anthony Forsyth, thoroughly examines four countries and industrial relations systems—Australia, the UK, the USA, and Italy—to assess and contrast their similarities and differences. The choice of these systems is not accidental. The author compares three industrial relations systems that are rooted in the common law and currently share a very fragmented collective bargaining structure with that of a civil law-based system that, despite recent attempts at decentralization, still maintains a more centralized structure. The interest of Anglophone scholars towards industrialized non-English speaking countries is relatively uncommon, making this element of the study original and valuable in itself in recent labour and industrial relations scholarship. In the book, Professor Forsyth offers a detailed and insightful description of the trends and developments in unionization and labour regulation in all four countries. His comparative analysis shows a deep understanding of the structures of their systems, their recent historical evolution, and the challenges they currently face, both shared and unique.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.045
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.010
Scholarly communication0.0150.015
Open science0.0010.007
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0450.006

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 head0.057
GPT teacher head0.329
Teacher spread0.272 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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