Corruption networks and anti-corruption mechanisms: the case of Kenya
Bibliographic record
Abstract
Purpose This paper explores how strong corruption networks, consisting of members of the Kenyan political elite, senior bureaucrats and their corporate cronies, work around anti-corruption mechanisms and render them ineffective. Design/methodology/approach We collect and analyse our data using multiple methods, including field interviews, direct observations and archival data from various sources. Findings Corruption persists in Kenya largely because of the lack of political will at various levels to operationalise proactive anti-corruption measures. Furthermore, the deeply entrenched sociocultural expectations and weak public sector governance structures give rise to inadequate transparency and accountability, resulting in more significant opportunities for corruption. Practical implications Undoubtedly, winning the fight against corruption in any setting (Africa or otherwise) requires strong political will. The fight against corruption needs a policy shift, with more emphasis on reactive anti-corruption mechanisms such as raids and surveillance, which again requires a strong political will. We propose that future research energies can be directed at understanding how political will in fighting corruption can be enhanced in sub-Saharan Africa. Originality/value We extend the literature on corruption by presenting a strong case that shows that enacting anti-corruption laws and regulations alone is not enough to eradicate corruption. We provide insights into how strong corruption networks can impede government programs and legitimise questionable practices that allow network members to make substantial private gains at the expense of the general population in an African setting.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".