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Record W4320917142 · doi:10.18280/ijsdp.180113

Environment and Social Framework: Compromise of Interest as Social Conflicts Resolution in Infrastructure Projects

2023· article· en· W4320917142 on OpenAlexvenueno aff
Heru Bayuaji Sanggoro, Sofia W. Alisjahbana, Dadang Mohamad

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCompromiseEnvironmental resource managementEnvironmental planningBusinessPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

This research is a development from the previous studies that aimed to test the ability of the World Bank's Environment and Social Framework (ESF) in minimizing the impact of social conflict projects in the context of environmental and social protection.The study used samples from infrastructure projects in Indonesia sourced from the State Budget throughout 2018-2021.There were 120 respondents who participated in this study by filling out a questionnaire.Meanwhile, secondary data is obtained through indexes and official government data regarding the condition of local communities that reflect the level of interest.Using PLS-SEM method, the results of significant influence from ESF in minimizing the potential impact of social conflicts in the project were obtained.ESF can effectively become a "compromise of interest" for the project and the community.This study also proves the independence of ESF, where both interests are unable to affect the quality of ESF.This proves that the position of ESF as an effective legal tool in managing environmental and social impacts needs to be strengthened by firm regulation.The results of this study are expected to help further research in finding differences in project social conflict behavior based on cultural differences in Indonesia.

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)0.002
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 score0.261
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.104
GPT teacher head0.357
Teacher spread0.253 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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