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USMCA Labour Provisions and Their Impact on Working Conditions in the Mexican Automotive Industry: An Initial Assessment

2024· article· en· W4406310406 on OpenAlexaboutno aff
María Gómez Ojeda, Nadine Reis

Bibliographic record

VenueJournal für Entwicklungspolitik · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryBusinessManufacturing engineeringEngineering

Abstract

fetched live from OpenAlex

This article examines the impact of the United States-Mexico-Canada Agreement's (USMCA's) labour provisions, specifically the Rapid Response Labor Mechanism (RRLM), on union dynamics within Mexico's automotive sector.Through an analysis of two case studies-the General Motors plant and auto parts manufacturer Draxton, both located in Central Mexico-we explore how the RRLM functions in the context of local power structures, global economic pressures, and the asymmetrical trade relationship between the United States and Mexico.While the RRLM has facilitated advancements, such as the establishment of an independent union at GM and subsequent improvements in working conditions, its limitations are evident in cases such as Draxton, where corporate resistance and weak institutional frameworks undermined efforts to achieve substantive reform.Using a multiscalar theoretical framework, we highlight the intersection of global governance mechanisms with local labour realities, revealing tensions between democratising union practices and the structural pressures to maintain economic competitiveness.The findings emphasise that, although the RRLM enhances the visibility of labour violations, its transformative potential is constrained by structural inequalities and localised resistance.We argue that sustainable labour reform in Mexico's automotive industry requires bridging global frameworks with robust local strategies to strengthen workers' capacities to challenge entrenched power asymmetries.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0000.002
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 head0.032
GPT teacher head0.374
Teacher spread0.342 · 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.

Study designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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