Deployment of whistleblowing as an accountability mechanism to curb corruption and fraud in a developing democracy
Bibliographic record
Abstract
Purpose This paper investigates the challenges and opportunities for the deployment of whistleblowing as an accountability mechanism to curb corruption and fraud in a developing country. Nigeria is the institutional setting for the study. Design/methodology/approach Adopting an institutional theory perspective and a survey protocol of urban residents in the country, the study presents evidence on the whistleblowing program introduced in 2016. Nigeria’s whistleblowing initiative targets all types of corruption, including corporate fraud. Findings This study finds that, even in the context of a developing country, whistleblowing is supported as an accountability mechanism, but the intervention lacks awareness, presents a high risk to whistleblowers and regulators, including the risk of physical elimination, and is fraught with institutional and operational challenges. In effect, awareness of whistleblowing laws, operational challenges and an institutional environment conducive to venality undermine the efficacy of whistleblowing in Nigeria. Originality/value The study presents a model of challenges and opportunities for whistleblowing in a developing democracy. The authors argue that the existence of a weak and complex institutional environment and the failure of program institutionalization explain those challenges and opportunities. The authors also argue that a culturally anchored and institutionalized whistleblowing program encourages positive civic behavior by incentivizing citizens to act as custodians of their resources, and it gives voice to the voiceless who have endured decades of severe hardship and loss of dignity due to corruption.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".