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Record W2612926762

A Campaign to End Female Genital Mutilation: An Argument Based on Common Grounds

2012· article· en· W2612926762 on OpenAlexaboutno aff
Tess A. Carlson

Bibliographic record

VenueThe Mathematics Enthusiast · 2012
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsnot available
Fundersnot available
KeywordsArgument (complex analysis)Political scienceMedicinePsychology
DOInot available

Abstract

fetched live from OpenAlex

Female genital mutilation (FGM) is a significant and longstanding tradition practiced by select cultures. It is also a serious human rights violation that has caused damaging psychological and physical pain to an estimated 100-140 million women throughout the world. FGM involves the surgical removal of all or part of female external genitalia and is thought by its practitioners to curb women’s sexual desire. Despite its widespread legal prohibition, FGM is still widely practiced in 28 African countries, parts of the Middle East and India, the United States, Canada, New Zealand, Australia, and Europe. In the summer of 2011, I had the opportunity to work with a non-governmental organization, the Foundation for Women’s Health Research and Development (FORWARD), on their campaign to end FGM. Through my work at FORWARD, I became committed to ending the practice of FGM by addressing the social constructs that perpetuate it. This paper describes and defends a certain method of advocacy, which I call the ‘grassroots approach,’ as the best way to abolish this practice. The grassroots approach works to create a shared awareness and understanding of FGM’s damaging effects, ultimately allowing women to make informed decisions on behalf of themselves and their families. By presenting this paper I hope to demonstrate that it is both possible and necessary to advocate for the abolition of harmful traditions in a way that is sensitive to cultural differences.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.045
GPT teacher head0.312
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2012
Admission routes1
Has abstractyes

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