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Record W2606888825 · doi:10.1080/00083968.2016.1277148

Whither the state? Mining codes and mineral resource governance in Africa

2017· article· en· W2606888825 on OpenAlexaffvenue
Terhemba Ambe-Uva

Bibliographic record

VenueCanadian Journal of African Studies / Revue canadienne des études africaines · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsCorporate governanceIndependence (probability theory)Natural resourceMineral resource classificationState (computer science)Resource curseResource (disambiguation)Political scienceDevelopmental stateGood governanceDevelopment economicsEconomic growthBusinessEconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

In contrast to the early post-independence era in which African states predominantly controlled the mining sector, the 1980s saw African countries update their mining codes to attract foreign capital. These reform measures largely diminished the power of the state, either resulting in its “selective silence” or its retraction. However, after three waves of these reforms, the disparity between natural resources and sustainable development has continued to widen. Two theories offer a nuanced approach to understanding the state of flux of mining codes and mineral governance in Africa: governance theory and the developmental state theory. This article argues that the activist, interventionist state is making a comeback in mineral resource governance throughout Africa. Moreover, regional initiatives such as the African Mining Vision represent a fundamental departure in mineral governance. However, such initiatives will only bring development to the extent that they are owned by African governments and backed by local communities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.011
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.211
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations35
Published2017
Admission routes2
Has abstractyes

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Same venueCanadian Journal of African Studies / Revue canadienne des études africainesSame topicMining and Resource ManagementFrench-language works237,207