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Record W4220664671 · doi:10.18280/ijsdp.170122

The World Bank - Environment and Social Framework: Expectations and Realities of Implementing Environmental and Social Safeguards in Infrastructure Projects in Indonesia

2022· article· en· W4220664671 on OpenAlexvenueno aff
Dadang Mohamad, Heru Bayuaji Sanggoro, Iwan Rustendi, Susatyo Adhi Pramono

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsModerationBusinessEnvironmental economicsClimate changeEnvironmental issueEnvironmental resource managementEnvironmental planningEconomicsPsychologyEnvironmental science

Abstract

fetched live from OpenAlex

Climate change is a result of the environmental degradation due to human activities and has the potential to become a climate disaster that threatens human life and causes social problems. Through the ESF, World Bank expect that infrastructure development can go hand in hand with environmental and social safeguards. To obtain information about the level of satisfaction of the performance of this ESF indicator, this study was conducted using the Importance Performance Analysis method to compare the expectations and the realities of the performance of the critical ESF indicators in Indonesia. The study involved 80 respondents of infrastructure project actors in Indonesia and found that 40% of the ESF indicators had performed well and met their expectations. Meanwhile, three indicators were inefficient or the performance exceeded the expectations, namely ESS6.3, ESS2.3 and ESS6.1. ESS7.2 and ESS8.1. This study is expected to contribute to developing a standardized and integrated ESF in Indonesia. Furthermore, the results of this study can be used as a consideration in further research, especially the use of the ESF variables as a moderator in modeling project social conflict.

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)0.001
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 score0.155
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.270
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 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

Citations6
Published2022
Admission routes1
Has abstractyes

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