An overview of female genital mutilation in Nigeria
Bibliographic record
Abstract
Nigeria, due to its large population, has the highest absolute number of female genital mutilation (FGM) worldwide, accounting for about one-quarter of the estimated 115-130 million circumcised women in the world. The objective of this review is to ascertain the current status of FGM in Nigeria. Pertinent literature on FGM retrieved from internet services [Google search on FGM in Nigeria, www.online Nigeria, PubMed of the national library of medicine www.medconsumer. Info/tropics/fgm.htm, Biomedcentral and African Journal Online (AJOL) (FGM)] and textbooks, journals, and selected references for proper understanding of the topic was included in this review. The national prevalence rate of FGM is 41% among adult women. Evidence abound that the prevalence of FGM is declining. The ongoing drive to eradicate FGM is tackled by World Health Organization, United Nations International Children Emergency Fund, Federation of International Obstetrics and Gynecology (FIGO), African Union, The economic commission for Africa, and many women organizations. However, there is no federal law banning FGM in Nigeria. There is need to eradicate FGM in Nigeria. Education of the general public at all levels with emphasis on the dangers and undesirability of FGM is paramount.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".