Harms of the current global anti-FGM campaign
Bibliographic record
Abstract
Traditional female genital practices, though long-standing in many cultures, have become the focus of an expansive global campaign against 'female genital mutilation' (FGM). In this article, we critically examine the harms produced by the anti-FGM discourse and policies, despite their grounding in human rights and health advocacy. We argue that a ubiquitous 'standard tale' obscures the diversity of practices, meanings and experiences among those affected. This discourse, driven by a heavily racialised and ethnocentric framework, has led to unintended but serious consequences: the erosion of trust in healthcare settings, the silencing of dissenting or nuanced community voices, racial profiling and disproportionate legal surveillance of migrant families. Moreover, we highlight a troubling double standard that legitimises comparable genital surgeries in Western contexts while condemning similar procedures in others. We call for more balanced and evidence-based journalism, policy and public discourse-ones that account for cultural complexity and avoid the reductive and stigmatising force of the term 'mutilation'. A re-evaluation of advocacy strategies is needed to ensure that they do not reproduce the very injustices they aim to challenge.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".