Do countries’ freedom status and gender equality level inform gender differences in bribery? Evidence from a multi-country level analysis
Bibliographic record
Abstract
Given the continuing debate on whether women are less corrupt than men, this study investigates the socio-political context in which men and women give bribes based on the seventh round of the Afrobarometer multi-country data set. We also seek to understand how a country’s freedom status and gender equality level inform the extent to which women and men are likely to be involved in corruption. In doing so, the study focuses on the influence of gender status, the number of female legislators, gender equality, and political freedom on bribe-giving among men and women. Research results indicate that (1) women in Africa are less likely to pay bribes than men, controlling both macro-level and micro-level factors, (2) women are less likely than men to give bribes in countries with high gender equality, and (3) the tendency for women to give bribes is the lowest in politically free countries. However, the inclination of women’s bribery reached the highest level among countries with partial political freedom. This study extends the theoretical and empirical understanding of the context within which women are more or less likely to give bribes, especially in the global South.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".