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Record W2252821796

Public participation and community involvement in environmental and social impact assessment in developing countries

2006· article· en· W2252821796 on OpenAlexfundno aff
Salim Momtaz

Bibliographic record

VenueNOVA (University of Newcastle Australia) · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
FundersUniversity of Illinois at Urbana-ChampaignUniversity of WaterlooUniversity of DhakaQueensland University of TechnologyUniversity of South AustraliaRMIT UniversityNational University of SingaporeIllinois State UniversityCentral Queensland UniversityGeorgetown UniversityGeorge Washington University
KeywordsSocial impact assessmentDeveloping countryPublic participationEnvironmental impact assessmentPolitical scienceSocial impactEconomic growthBusinessEnvironmental planningPublic relationsSociologyEconomicsGeographyPopulation
DOInot available

Abstract

fetched live from OpenAlex

Involving community in decision making process is an integral part of environmental and social impact assessment(EIA and SIA) in developed countries. While EIA and SIA have now been firmly established in planning process in developing countries, community participation in project inception, implementation and monitoring remains a contentious issue. Recently, we have seen the adoption of Vroom-Yetton normative decision model in developing guidelines for managers that allow them to determine the level of public involvement in natural resource decision making. This paper examines the adaptability of Vroom-Yetton model in EIA and SIA processes in Bangladesh. Based on thorough examinations of major EIA guidelines and a number of recent EIAs and SIAs, and discussions with EIA practitioners in Bangladesh, this paper, firstly, explores the status of community participation in EIA and SIA in Bangladesh, and, secondly, it examines if Vroom-Yetton model can make any meaningful contribution to deciding the extent to which local communities may be incorporated in environmental assessment process. This article argues that there is no substitute for wide community consultation and participation in a densely populated country like Bangladesh where any development interventions are likely to have significant impacts on people and the natural environment.

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 score0.006
Threshold uncertainty score0.997

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.172
GPT teacher head0.349
Teacher spread0.177 · 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

Citations10
Published2006
Admission routes1
Has abstractyes

Explore more

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