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Record W4214632854 · doi:10.1080/14615517.2022.2035646

A roadmap for ESIA policy change in Ethiopia should address wide-ranging governance reforms

2022· article· en· W4214632854 on OpenAlexaffabout
Melisha Charles, Joshua Tafel, Doug McDonnell, C. Stoicheff, Nadja C. Kunz

Bibliographic record

VenueImpact Assessment and Project Appraisal · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British Columbia
FundersAustralian Government
KeywordsTransparency (behavior)AccountabilityCivil societyLegislatureCorporate governanceGovernment (linguistics)Natural resourceBusinessSustainable developmentPublic policyEnvironmental planningEnvironmental resource managementEconomic growthPolitical scienceEconomicsGeographyFinancePolitics

Abstract

fetched live from OpenAlex

Robust governance frameworks are crucial for maximizing the benefits of natural resource development in mining regions and minimising adverse social and environmental impacts. This paper analyses Ethiopia’s current ESIA policy, legal framework and implementation process to identify opportunities for policy reform. To gain perspective on leading practices in other countries, a comparative analysis of policies in Chile, Peru, Ghana, and Canada was performed. Interviews with representatives from government, industry, and civil society in Ethiopia were completed to gain perspective on the policy structures currently in place, and to identify areas for improvement. Results conclude that accountability mechanisms are currently limited in Ethiopia and that at the time of this study, the same authority was responsible for promoting mining and evaluating ESIAs. Contrary to legislative requirements, there was found to be a lack of transparency in the public availability of ESIAs and limited community participation. Compliance and monitoring processes were also found to be inadequate. Addressing these gaps is important to ensure that the expansion of Ethiopia’s mining sector proceeds in a sustainable manner.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.824

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 studies0.0000.000
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 head0.052
GPT teacher head0.399
Teacher spread0.347 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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