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Record W2951888583 · doi:10.1177/0262728019843715

Participatory Democracy or State-Induced Violence? Resettling the Displaced People of Hatirjheel in Dhaka

2019· article· en· W2951888583 on OpenAlexaff
Farzana Quader Nijhum, Sk. Towhidur Rahaman, Mohd. Jamal Hossain, Ishrat Islam

Bibliographic record

VenueSouth Asia Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHydropower, Displacement, Environmental Impact
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRelocationCitizen journalismState (computer science)Economic JusticeParticipatory planningSociologyDemocracySettlement (finance)Corporate governancePoliticsParticipatory developmentResource (disambiguation)Forced migrationPublic relationsPolitical sciencePublic administrationEconomic growthLawBusinessRefugeeEconomicsManagement

Abstract

fetched live from OpenAlex

This article discusses the trajectory of project implementation in the development of the Hatirjheel lake area in Dhaka, which involved forced relocation and socio-economic deprivation for most project-affected people. It raises questions over the extent to which such processes need to be seen as state-induced violations of basic justice, asking whether more justice-focused management of such projects is becoming an unrealistic expectation in an increasingly crowded Bangladesh. The article discusses the socio-political dynamics and community-related issues affecting different stakeholders during the implementation of the project. Despite the official presence of participatory planning techniques, the forceful imposition of the development plans and the drastic ramifications of forced land acquisition are shown to have violated basic principles of good governance. It is suggested that less violent and more inclusive approaches are possible despite resource scarcities and that lessons can be learned from such experiences for the future.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.008
Scholarly communication0.0020.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.109
GPT teacher head0.501
Teacher spread0.391 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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