Whistleblowing and Anti-Corruption Crusade: Evidence From Nigeria
Bibliographic record
Abstract
The legal framework to fighting corruption has been less effective probably because it is evidence-based. This study investigated the extent to which whistleblowing predicted Corruption Perception Index (CPI) score, the extent to which employees engaged in whistleblowing, and why they did not report cases of corruption. Respondents were 536 employees (298 males; 238 females) sampled from public organizations in southwestern Nigeria. Their ages averaged 32.13 (SD =9.78). Results of the simple regression indicated that the country’s CPI score significantly increased with whistleblowing. Results of the χ2 test indicated that majority of the respondents were discouraged from blowing the whistle because of social and psychological factors such as the feelings that whistleblowing would not bring desired change, fear of being ostracized, the perceived stress associated with being a witness to corrupt acts, perceived inefficiency of the court process, the perceived clumsiness in the whistleblowing process, the feelings that corrupt persons were too powerful, and the feelings that corruption had no direct victims. There were no gender differences in the effect of the social and psychological factors in whistleblowing. Similarly, there were no gender differences in the frequency of whistleblowing from 2001 to 2012. However, in the 2013 (January to June) significant gender differences occurred in the frequency of whistleblowing with more females engaging in whistleblowing than males. Implications of the findings for theory and research were discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".