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Record W4281996043 · doi:10.1080/13549839.2022.2078293

Development for whom?: an Indigenous environmental justice movement in Bangladesh

2022· article· en· W4281996043 on OpenAlexaff
Mohammad Mahmud Hasan

Bibliographic record

VenueLocal Environment · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsCarleton University
Fundersnot available
KeywordsEnvironmental justiceIndigenousMovement (music)Economic JusticeClimate justicePolitical scienceEnvironmental movementGeographySociologyEnvironmental planningEnvironmental resource managementEconomic growthDevelopment economicsEnvironmental ethicsClimate changePoliticsEconomicsLawEcology

Abstract

fetched live from OpenAlex

Indigenous peoples around the world tend to be disproportionately affected by resource extraction activities having access to fewer technical, legal and other resources to participate effectively in the decision-making process. Taking a resistance movement concerning the Phulbari Coal Project in Bangladesh, through qualitative research, this paper examines how Indigenous peoples frame their claims in a mining conflict situation. The Phulbari resistance movement took place more than a decade ago in Bangladesh, however, local Indigenous peoples, Bangalee farming communities, and activists still bear the spirit of the movement. As such, this study seeks to test the claims of how the Indigenous and farming communities, and the actions of national and transnational environmental justice organisations in the Phulbari resistance movement form part of an environmental justice movement. The paper argues that various components of Indigenous resistance and claims may contribute to the overall goals of the environment justice movement against powerful transnational corporations in the global South.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.009
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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 designQualitative
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

Citations3
Published2022
Admission routes1
Has abstractyes

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