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Record W4378388766 · doi:10.1017/lap.2022.63

Lost in Corporate Translation: How Firms Mediate Between Social Mobilization and Regulatory Intervention in the Extractive Sector

2023· article· en· W4378388766 on OpenAlexaff
Paul Alexander Haslam, Julieta Godfrid

Bibliographic record

VenueLatin American Politics and Society · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLatin AmericansCohesion (chemistry)Argument (complex analysis)MobilizationIntervention (counseling)Social movementState (computer science)Resource mobilizationCivil societyLimitingPolitical economyPolitical sciencePublic relationsSociologyLaw

Abstract

fetched live from OpenAlex

ABSTRACT Firms should be considered as actors that potentially mediate between social movement pressures and policy outcomes. This article shows that at the mining project level, social mobilization can generate important changes in corporate practices toward nearby communities, and that these practices can undermine the cohesion of social movement coalitions advocating for regulatory intervention or reform, thus limiting their ability to make compelling claims on the state. In this way, company interpretations of and responses to protest are an important mediating process that conditions civil society efforts to activate state institutions in their favor. This argument extends recent work on the social foundations of regulation in Latin America by including corporate actors. The article is based on a comparative case study of the Pascua Lama/Veladero mining projects in both Argentina and Chile, using both secondary sources and primary field research.

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 score0.462
Threshold uncertainty score0.260

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 studies0.0000.000
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 head0.031
GPT teacher head0.246
Teacher spread0.215 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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