A Human Rights-based Approach to Combating Public Procurement Corruption in Africa
Bibliographic record
Abstract
Corruption is a threat to human rights as it erodes accountability and violates many international human rights conventions. It also undermines basic principles and values like equality, non-discrimination, human dignity, and social justice – especially in African countries where democratic systems and institutional arrangements are less developed than in most European, Asian and American countries. Corruption occurs in both the public and private sectors and affects human rights by deteriorating institutions and diminishing public trust in government. Corruption impairs the ability of governments to fulfil their obligations and ensure accountability in the implementation and protection of human rights – particularly socio-economic rights pertinent to the delivery of economic and social services. This is because corruption diverts funds into private pockets – impeding delivery of services, and thereby perpetuating inequality, injustice and unfairness. This considered, the focus of this paper is on public procurement corruption. It is argued that by applying a human rights-based approach to combating public procurement corruption, the violation of human rights – particularly socio-economic rights – can be significantly reduced. Through a human rights-based approach, ordinary people can be empowered to demand transparency, accountability and responsibility from elected representatives and public officials – particularly those involved in public procurement. In the paper, reference is made to selected aspects of the national legal frameworks of five African countries: South Africa, Uganda, Kenya, Nigeria and Botswana.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".