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Record W2027566721 · doi:10.1300/j115v19n04_07

Controversial Issues

2000· article· en· W2027566721 on OpenAlexaboutno aff
Diane Richards

Bibliographic record

VenueMedical Reference Services Quarterly · 2000
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingMulticulturalismImmigrationThe InternetHealth professionalsFemale circumcisionMedicineHealth careMedical practiceCultural safetyNursingPublic relationsFamily medicinePsychologySocial psychologyPolitical scienceGynecologyWorld Wide Web

Abstract

fetched live from OpenAlex

As immigrant women from African countries enter the U.S., Canada, Australia, and Western Europe, western health care providers are beginning to see patients affected by the cultural practice of Female Genital Mutilation (FGM). Unfamiliar with the practice, either medically or culturally, these providers are turning to medical librarians for information. Complicating the issue are the strong negative feelings most western health care workers have about FGM, which appears to them to be both barbaric and cruel. These feelings may conflict strongly with those of their immigrant patients, who regard the practice as normal and desirable. Both medical and cultural information are needed for the professional to provide treatment of medical conditions, while also establishing a good relationship with the FGM affected patient. This article identifies and describes the most important refereed journal article databases, available now over the Internet, providing both medical and cultural information on FGM, and the most useful Web sites for health professionals, librarians, and interested laypersons who need information about this difficult multicultural issue.

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)0.021
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.097
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0080.009
Scholarly communication0.0100.010
Open science0.0040.006
Research integrity0.0340.020
Insufficient payload (model declined to judge)0.0440.011

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 head0.011
GPT teacher head0.296
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations5
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueMedical Reference Services QuarterlySame topicFemale Genital Mutilation/Cutting IssuesFrench-language works237,207