Informal Networks of Corruption: Assessing the Challenges for Public Sector Whistleblowing in Nigeria
Bibliographic record
Abstract
Recently, the Nigerian government adopted its first National Anti-Corruption Strategy—the first since its independence in 1960. While the strategy captures varying forms of corruption, whistleblowing is seen as one of the key strategies identified to confront anti-corruption in the public sector. The adoption of the whistleblowing policy and its on-going implementation however occurs without a legislative framework to protect whistleblowers. This article situates the whistleblower program in the wider socio-political context of anti-corruption in Nigeria, and public governance. The paper critically examines the implications of the legislative gaps for the long-term sustenance of the whistleblower protection program. This paper argues that the whistleblowing program is embedded in the wider socio-political and informal social norms that have historically privileged corruption in Nigeria. To enhance the overall effectiveness and institutionalization of the whistleblowing program in Nigeria, this paper contends that the urgent adoption of a comprehensive legislative protection framework is a minimum requirement. Significant practical steps must be taken to address the complex background of informal social networks of corruption, power dynamics, and social norms that are peculiar to the Nigerian economic and political context.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".