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Record W4388068211 · doi:10.1080/03057070.2023.2268487

Resource Nationalism in Zimbabwe: Alternative Visions and Policy Realities

2023· article· en· W4388068211 on OpenAlexaff
Richard Saunders, Lyman Mlambo, Jesse Salah Ovadia

Bibliographic record

VenueJournal of Southern African Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican studies and sociopolitical issues
Canadian institutionsUniversity of WindsorYork University
Fundersnot available
KeywordsResource curseAccountabilityNationalismPoliticsPolitical economyCivil societyTransparency (behavior)Corporate governanceCommodityRedistribution (election)LegitimacyVisionContext (archaeology)Natural resourcePolitical scienceResource (disambiguation)EconomicsEconomic growthSociologyMarket economyLawGeography

Abstract

fetched live from OpenAlex

A new wave of resource nationalism washed through southern Africa in the 2000s, driven by rising popular demand for greater local participation in the mining sector value chain, more equitable redistribution of benefits from extractives and strengthened transparency and accountability on the part of local states and foreign-owned mining companies. In the context of heightened political contestation and improved commodity prices, a succession of policy innovations emerged in response to the interventions of diverse mining sector stakeholders and civil society activists. The results have often been disappointing, with the initial promise of an ‘alternative’ form of minerals-fuelled development promoted by non-state beneficiaries seemingly unrealised. To understand the challenges of current policy-making around resource nationalism we consider the case of Zimbabwe, a country which has been host to vibrant mining reform debates and experiments in new policy-making in the 2000s, but has experienced little transformation in resource governance practices. This article assesses the critical factors which have contributed to the generation and subsequent derailing of new approaches to resource nationalism during a period of exceptional growth in the country’s mining sector.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.066
GPT teacher head0.404
Teacher spread0.338 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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