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Record W4301277229 · doi:10.51952/9781447325741.ch006

F

2016· book-chapter· en· W4301277229 on OpenAlexaboutno aff

Bibliographic record

VenuePolicy Press eBooks · 2016
Typebook-chapter
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The term ‘female genital mutilation’ (FGM) (also termed ‘female genital cutting’) is defined as ‘all procedures involving partial or complete removal of the external female genitalia or other injury to the female genital organs, whether for cultural or any other non-therapeutic reasons’ (WHO, 1997, p 1). There are several forms of FGM; however, they fall broadly into three main forms: Type 1, clitoridectomy, where all or part of the clitoris is removed; Type 2, excision, where all or part of the clitoris and labia are removed; and Type 3, which consists of infibulation with excision. The World Health Organisation (WHO) reports that Types 1 and 2 are the most common forms of FGM globally, with current estimates suggesting that around 90% of FGM cases include Types 1 and 2. FGM is a practice usually performed by untrained ‘midwives’ who lack the requisite medical expertise to deal with complications. It is a procedure that is commonly performed in unsanitary conditions with unsterilised instruments, many of which are non-medical instruments such as glass, blades and other sharp or sharpened objects. In some cases, the practice is performed by medical professionals under sanitary conditions; however, this is usually in cases where the girls and young women come from wealthy families. It is a common practice in regions of Africa, Asia and the Middle East, with an estimated 100 million to 140 million girls and women being victims of FGM (WHO, 2008). Although FGM is traditionally practised in these countries, the WHO (2008) declared that the practice by immigrants has made it a public health issue in Europe, Canada, Australia and the US.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.070
GPT teacher head0.323
Teacher spread0.253 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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