A Campaign to End Female Genital Mutilation: An Argument Based on Common Grounds
Bibliographic record
Abstract
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 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 | 0.031 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.038 | 0.042 |
| 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".