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Voices at Work

2014· book· en· W4241275272 on OpenAlexaboutno aff
Alan Bogg, Tonia Novitz

Bibliographic record

VenueOxford University Press eBooks · 2014
Typebook
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsMultitudeLabour lawLegislaturePoliticsScope (computer science)Political scienceWork (physics)LawCommon lawSociologyLaw and economicsEngineering

Abstract

fetched live from OpenAlex

This edited collection is the culmination of a comparative project on 'Voices at Work' funded by the Leverhulme Trust 2010–2013. The book aims to shed light on the problematic concept of worker 'voice' by tracking its complex interactions with various forms of law. Contributors to the volume identify the scope for continuity of legal approaches to voice and the potential for change in a sample of industrialized English speaking common law countries, namely Australia, Canada, New Zealand, UK, and USA. These countries, facing broadly similar regulatory dilemmas, have often sought to borrow and adapt certain legal mechanisms from one another. The variance in the outcomes of any attempts at 'borrowing' seems to demonstrate that, despite apparent membership of a 'common law' family, there are significant differences between industrial systems and constitutional traditions, thereby casting doubt on the notion that there are definitive legal solutions which can be applied through transplantation. Instead, it seems worth studying the diverse possibilities for worker voice offered in divergent contexts, not only through traditional forms of labour law, but also such alternative disciplines.. This book comprises contributions from many leading scholars of labour law, politics, and industrial relations drawn from across the jurisdictions. It is addressed to academics, policy makers, legal practitioners, legislative drafters, trade unions, and interest groups alike. Additionally, while offering a critique of existing laws, this book proposes alternative legal tools to promote engagement with a multitude of 'voices' at work.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.816
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.019
GPT teacher head0.229
Teacher spread0.209 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations29
Published2014
Admission routes1
Has abstractyes

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