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Record W2339730393 · doi:10.1111/basr.12081

A <scp>S</scp>wiss‐Army Knife? A Critical Assessment of the Extractive Industries Transparency Initiative (EITI) in <scp>G</scp>hana

2016· article· en· W2339730393 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBusiness and Society Review · 2016
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Alberta
FundersBrock UniversityPierre Elliott Trudeau Foundation
KeywordsTransparency (behavior)AccountabilityArgument (complex analysis)Context (archaeology)Function (biology)AccountingBusinessCorporate social responsibilityFinancial sectorPublic relationsPolitical scienceLaw and economicsEconomicsLawFinanceGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Within the current global atmosphere where a universally accepted police force is nonexistent, there are several voluntary norms and codes of conduct that exist to guide how corporations behave worldwide. These have come as a result of many years of poor performance in the areas of social, financial, and environmental responsibility. Such norms are expected to prescribe and proscribe certain types of corporate behavior but when one examines the reality on the ground, the story is not that straightforward. This article assesses the effectiveness of the Extractive Industries Transparency Initiative (EITI) in the Ghanaian context with a focus on the mining sector. Based on primary qualitative data the argument is that even though the EITI is performing some function, it has ways to go before it can become an across‐the‐board viable tool for transparency and proper accountability. Five prevailing weaknesses are discussed to underscore this case.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.277
Teacher spread0.244 · 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