Shadows of Inequality: Exploring the Prevalence and Factors of Discrimination and Harassment in Nigeria
Bibliographic record
Abstract
Discrimination and harassment (DH) against women are topics of broad concern to gender equality advocates. This study aimed to investigate the prevalence of DH against women in Nigeria, based on seven specific forms of DH captured in the 2021 Nigeria Multiple Indicator Cluster Survey (MICS), and to identify key socio-demographic factors associated with an aggregated DH outcome variable. Drawing upon data from 38,806 women aged 15–49, we used descriptive statistics to summarize the prevalence of DH across seven reasons and the socio-demographic characteristics of respondents, followed by chi-square analysis to test bivariate associations and binary logistic regression to identify predictors. Results showed that the prevalence of DH against Nigerian women (18.9%) was significantly associated with socio-demographic factors such as age, education level, wealth index, marital status, and ethnicity. At the individual level, women who felt very unhappy had higher odds of experiencing DH (OR = 3.101, 95% CI: 2.393–4.018, p < 0.001) compared to those who felt very happy. In contrast, women with higher/tertiary education (OR = 0.686, 95% CI: 0.560–0.842, p < 0.001) were 31.4% less likely to face DH than those with no education. Regionally, respondents living in Zamfara (OR = 5.045, 95% CI: 3.072–8.288, p < 0.001) were over five times more likely to experience DH than those in Kano state. The findings underscore the need for policy interventions and support systems to address DH against women in Nigeria.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".