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Record W2155790002 · doi:10.1162/glep_a_00213

Transparency in Resource Governance: The Pitfalls and Potential of “New Oil” in Sub-Saharan Africa

2014· article· en· W2155790002 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.

Bibliographic record

VenueGlobal Environmental Politics · 2014
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsTransparency (behavior)Corporate governanceBusinessResource (disambiguation)StakeholderTransformative learningEconomicsPolitical sciencePublic relationsSociologyLawFinance

Abstract

fetched live from OpenAlex

An international agenda has evolved over the past decade to establish hard and soft rules to govern the impacts of the extractive industries. The international community and some resource-rich states have increasingly embraced norms such as transparency in resource governance. This paper explores how multi-stakeholder initiatives such as the Extractive Industry Transparency Initiative (EITI) and the Publish What You Pay (PWYP) campaign have sought to institutionalize transparency in resource governance. By exploring how, why, and to what effect transparency in resource governance has taken hold in a new petro-economy such as Ghana, I highlight two key findings: the interaction between voluntary and mandatory governance mechanisms and rescaling of authority, and the multi-scalar dimensions of resource governance and subsequent lack of focus on sub-national issues. In concluding, I question the transformative potential of transparency in resource governance, which has significant global implications as the demand for energy and non-energy minerals continues to rise.

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)0.000
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 score0.598
Threshold uncertainty score0.419

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 communication0.0000.000
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 head0.004
GPT teacher head0.165
Teacher spread0.160 · 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