An Examination of Political Patronage and Maladministration on State-Owned Entities with Specific Reference to South African Airways: A Literature Study
Bibliographic record
Abstract
South Africa is one of the states across the globe that is rattled with corruption and maladministration in multiple state institutions. The high rates of corruption within the government were alarming with the local, provincial, and national levels of governance such that multiple measures and task teams were established to combat this discourse. However, this has in recent years seen the spike in corruption now overlapping the State-Owned Entities (SoEs) and not so much in governmental administration. Political elites have used their influence to penetrate the state's entities to either loot their resources, cause nepotism, or harvest tenders in a corrupt manner. Thus, political patronage has allowed the occupation of these entities due to relations at the party level which escalates to business relations. Thus, cadre deployment of less skilled individuals based on political affiliations has led to corruption and maladministration of most state entities such as South Africa Airways (SAA). Through relying on qualitative methods, specifically existing literature and various official documents which describes the effects of political patronage and maladministration on SoEs with specific reference to SAA, this paper reveals that political patronage has contributed to maladministration of SoEs. Thus, due to political patronage and maladministration SAA has failed to be an epitome of effectiveness in discharging its legislative duties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".