Female Genital Mutilation in Nigeria: A Persisting Challenge for Women’s Rights
Bibliographic record
Abstract
Although considered a violation of human rights, female genital mutilation (FGM) is a commonly accepted practice in Nigeria in the ritual and sociocultural context of the population. In recent years, there have been strong policy actions by Nigerian legislature to curb this practice. Despite that, FGM continues to be a widespread phenomenon. In this study, we aimed to report on the prevalence of FGM, women’s attitude towards this practice, and its association with selected sociodemographic factors. Methods: Nigeria Demographic and Health Surveys conducted in 2003, 2008 and 2013 provided the data for this study. The participants were married women aged between 15 and 49 years. Owing to the clustered nature of the data, a complex survey plan was created to account for cluster effects and sampling weights. Data were analysed using bivariate and multivariate regression techniques. Results: Overall prevalence of FGM was 38.9% (95% CI = 36.4–40.1), and that among their daughters was 17.4% (95% CI = 15.3–19.7). There has been a substantial increase in the prevalence of FGM in 2013 compared to its 2003 level. Respondents who had undergone circumcision were more likely to have their daughters circumcised. In all three surveys, almost all of the circumcisions were performed by traditional practitioners. In the regression analysis, respondent’s age, area and region of residency, religious affiliation, educational status, and household wealth appeared to be significant predictors of FGM. Conclusion: In Nigeria, FGM remains a widely prevalent phenomenon with an increasing number of women experiencing this practice. Important regional and socioeconomic disparities were observed in the prevalence which merit urgent policy attention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".