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Record W4412823644 · doi:10.1002/tie.70018

State Capture—Institutionalized Corruption of the South African Revenue Service

2025· article· en· W4412823644 on OpenAlexafffund
Jennifer H. Heckel, Prescott C. Ensign

Bibliographic record

VenueThunderbird International Business Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLanguage changeState (computer science)RevenueBusinessService (business)AccountingMarketingComputer science

Abstract

fetched live from OpenAlex

ABSTRACT This study provides valuable insight for scholars, practitioners, and government officials on how a local office of a respected international consulting firm became associated with allegations of illicit influence and state capture. The article presents how political and business leaders used a government agency, the South African Revenue Service (SARS), to gain power, amass wealth, and find protection from prosecution. It outlines how illegal actions by public officials sworn to uphold justice and the rule of law harmed the Republic of South Africa. It also examines how a consulting firm that values integrity and trustworthiness contributed to questionable decisions by state officials. The impact at SARS included: bypassing government procurement procedures; restructuring and staffing that dismantled tax code enforcement; distributing public funds into private hands; and avoiding public accountability. The study concludes with a synopsis of the multiple investigations and implications for ethical behavior in an era of institutionalized state capture.

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)0.000
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: none
Teacher disagreement score0.946
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.319
Teacher spread0.283 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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