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Difficult but Not Impossible: The ANC's Decentralization Strategy in South Africa

2004· article· en· W2100324937 on OpenAlexaff
George Niksic

Bibliographic record

VenueDevelopment and Change · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsYork University
Fundersnot available
KeywordsDecentralizationDisadvantagedDispose patternMetropolitan areaPublic administrationGlobeEconomic growthPopulationGovernment (linguistics)Private sectorLocal governmentService delivery frameworkService (business)Political scienceEconomicsSociologyEconomyEngineeringMarket economy

Abstract

fetched live from OpenAlex

Abstract As the neo‐liberal project has spread across the globe, decentralization has been a key component of it. In South Africa, the neo‐liberal macro‐economic strategy of the African National Congress (ANC) involves support for fiscal and administrative decentralization partly as a way to bring the private sector into basic service delivery and supposedly to make local government more efficient and effective. However, the ANC also sees decentralization as a way to empower the historically disadvantaged black population. Community‐based public–private partnerships have been one of the chief initiatives in this regard. In the metropolitan municipality of Port Elizabeth, small black contracting companies have been hired and trained to dispose of waste, construct roads and build houses. While not free of tensions and problems, this approach to decentralization has fostered a form of democratic development. This article uses examples from the Port Elizabeth experience to test and reflect upon a number of issues which are raised in the literature on decentralization.

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.009
Scholarly communication0.0060.004
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.070
GPT teacher head0.291
Teacher spread0.221 · 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 designNot applicable
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

Citations20
Published2004
Admission routes1
Has abstractyes

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