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Record W1721000460 · doi:10.1089/env.2014.0013

Solidarity after Bhopal: Building a Transnational Environmental Justice Movement

2014· article· en· W1721000460 on OpenAlexfundno aff
Renu Pariyadath, Reena Shadaan

Bibliographic record

VenueEnvironmental Justice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
FundersMcMaster University
KeywordsSolidarityCorporationEnvironmental justiceDiasporaPolitical scienceEconomic JusticeSocial movementGovernment (linguistics)Resistance (ecology)Work (physics)Public administrationLawSociologyEngineeringPolitics

Abstract

fetched live from OpenAlex

This article documents the history of the U.S.-based campaign that emerged around the ongoing Bhopal disaster, since the 2001 merger of Union Carbide Corporation (Union Carbide) and the Dow Chemical Company (Dow). Based on interviews with key organizers and former and current campaigners in the United States and in Bhopal, the article discusses how this movement has worked to keep the Bhopal disaster alive and relevant for its three target constituencies in the United States. By appealing to social and environmental justice (EJ) groups, students, and the Indian diaspora, the campaign has won small victories in India and has challenged Dow's greenwashing attempts. Members of the diaspora have been instrumental in setting in motion what scholars have called the boomerang effect through exerting pressure on the Indian government. We also see the double boomerang at work in the United States when EJ activists make strategic references to Bhopal in times of crisis. More needs to be done, however, to build a sustained transnational EJ resistance to acknowledge the ongoing impact of toxics on people and their environments.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0360.021
Scholarly communication0.0110.009
Open science0.0020.031
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.011
GPT teacher head0.272
Teacher spread0.260 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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