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Record W4403888220 · doi:10.1177/00221856241278989

Between labour control and worker empowerment: Authoritarian innovations and democratic reforms in Mexico

2024· article· en· W4403888220 on OpenAlexaboutno aff
Mark Anner, Matthew Fischer-Daly, Cirila Quintero Ramírez

Bibliographic record

VenueJournal of Industrial Relations · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsAuthoritarianismDemocracyControl (management)EmpowermentPolitical scienceIndustrial democracyLabour economicsEconomicsEconomic systemPolitical economyManagementPoliticsLaw

Abstract

fetched live from OpenAlex

This paper analyses authoritarian innovations in the industrial relations arena in Mexico since 2018. Historically, corporatist ties with union elites allowed the government to control labour at the workplace level and resist substantive labour reforms at the national level through ‘ghost unions’ and ‘protection contracts’. Since the election of Andrés Manuel López Obrador of the left-oriented MORENA party, the government has implemented labour reforms and a reformed trade and investment treaty with the US and Canada that includes stronger labour provisions. These changes opened new possibilities for independent, democratic, and strong unionism and thus the potential for worker empowerment. Yet authoritarian innovations embedded in the national and regional reforms have limited labour's power. These meso-level mechanisms include: state bureaucratic control over union formation, collective bargaining and the right to strike; 2 state support for incumbent union control over worker voting processes; and exclusion of sectors from access to redress under an inter-state trade and investment treaty (USMCA). The authors explore this argument through case studies involving agriculture, auto parts, and maquiladora workers in three regions of Mexico.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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