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Record W2809405750 · doi:10.1177/0308518x18784019

Labor geographies of socially embedded work: The multi scalar resistance of Mexican teachers

2018· article· en· W2809405750 on OpenAlexafffund
Paul Bocking

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSituatedAutonomyAgency (philosophy)PoliticsResistance (ecology)Collective actionPower (physics)Political scienceCorporate governanceState (computer science)SociologyPublic administrationWork (physics)Public relationsSocial scienceManagementLawEconomics

Abstract

fetched live from OpenAlex

Public sector workers experience particular challenges from the state when they organize and take collective action. Accountable to administrators as well as parents, teachers are embedded within complex power relations at scales from the classroom to the district and the state or nation. This article draws on labor geography’s understandings of how worker agency is socially situated, to explore how the capacities for protest of dissident elementary and secondary teachers in Mexico City have been limited. These obstacles are found within their workplaces governed by the local Secretary of Public Education, in broader political dynamics within the city and in a centralization of governance over education policy to the national level. As a result, between 2013 and 2016 , teachers here were less likely to join protests against policies initiated by President Enrique Peña Nieto that were widely deemed harmful to their professional autonomy, and which drew strong resistance in other regions of the country. This article concludes by briefly assessing how, as Peña Nieto’s term concluded, dissident teachers turned towards the national election and an equivocal relationship with the center-left Morena party.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.430

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.266
Teacher spread0.248 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

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