Mobs who Stripped Female Robbers but Failed to Strip the Male Robbers: The “Evil Women Hypothesis” on Nigerian Streets
Bibliographic record
Abstract
Academic reports on the “evil women” hypothesis have focused mainly on the actions of criminal justice authorities (CJA). However, actions based on this hypothesis equally extend to ordinary members of the public. Vigilante justice on suspected criminals by mobs is a regular occurrence in Nigeria. Thus, the current article drew on vigilante justice on robbers to examine the notion of the evil women hypothesis from the perspective of mobs. Three robbery incidents in three different Nigerian cities involving robbers impersonating taxi operators known as “one-chance” robbers, were analyzed. In all the three incidents, the one-chance robbers comprising both men and women were caught by mobs. In all of them, the mobs stripped the female robbers naked in public whilst their male gang members were allowed to wear their clothes. The actions of the mob conformed to those of CJA with respect to the evil women hypothesis whereby female offenders are punished more severely than their male counterparts due to the idea that the former have crossed the morality boundary to commit a serious offense that goes against the gender-role expectations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".