MétaCan
Menu
Back to cohort
Record W4234137178 · doi:10.31219/osf.io/4dxtf

Female genital mutilation: A challenge to Health Education

2020· preprint· en· W4234137178 on OpenAlexaboutno aff
Nkiru Onyinyechukwu NNAEMEZIE, Emeka

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsnot available
Fundersnot available
KeywordsFemale circumcisionQuarter (Canadian coin)Sex organPopulationEthnic groupDemographyObligationMedicineGender studiesGynecologySocioeconomicsGeographyPolitical scienceSociologyLawBiology

Abstract

fetched live from OpenAlex

Female genital mutilation (FGM), also known as female cutting and female circumcision, is the ritual removal of some or all the external female genitalia, typically carried out by a traditional circumcised with a blade or razor, with or without anaesthesia. FGM is practiced by ethnic groups in 27 countries of which Nigeria is one. The practice is rooted in gender inequality, attempts to control women's sexually, idea about purity, modesty and aesthetics , ànd a sense of obligation. FGM has been outlawed or restricted in most of the counties where it occurs, but the laws are poorly enforced. Nigerian, 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. Type I and Type II are more wide spread but less harmful compared to type III ànd then Type IV. There is need to eradicate FGM in Nigeria.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0070.006
Open science0.0020.006
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.0390.008

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.

Opus teacher head0.082
GPT teacher head0.378
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicFemale Genital Mutilation/Cutting IssuesFrench-language works237,207