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Record W2892615542 · doi:10.15173/glj.v9i3.3354

The Trade-Labour Nexus: Global Value Chains and Labour Provisions in European Union Free Trade Agreements

2018· article· en· W2892615542 on OpenAlexvenueno aff
Mirela Barbu, Liam Campling, Adrian Smith, James Harrison, Ben Richardson

Bibliographic record

VenueGlobal Labour Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
FundersEconomic and Social Research CouncilEuropean CommissionQueen Mary University of London
KeywordsNexus (standard)European unionValue (mathematics)International tradeCorporate governanceEconomicsFree tradeInternational economicsPoliticsTrade unionBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

Labour standards provisions contained within the European Union’s (EU) free trade agreements (FTAs) are a major iteration of attempts to regulate working conditions in the global economy. This article develops an analysis of how the legal and institutional mechanisms established by these FTAs intersect with global value chain governance dynamics in counoutries with contrasting political economies. The article formulates an original analytical framework to explore how governance arrangements and power relations between lead firms in core markets and suppliers in FTA signatory countries shape and constrain the effectiveness of labour provisions in FTAs. This analysis demonstrates how the common framework of labour provisions in EU trade agreements, when applied in a uniform manner across differentiated political-economic contexts, face serious difficulties in creating meaningful change for workers in global value chains.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0040.037
Scholarly communication0.0130.014
Open science0.0010.008
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.260
Teacher spread0.247 · 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 designQualitative
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

Citations18
Published2018
Admission routes1
Has abstractyes

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