From Body to Brain: Considering the Neurobiological Effects of Female Genital Cutting
Bibliographic record
Abstract
Female genital cutting (FGC) is an ancient tradition unbounded by religion and practiced primarily in Africa and the regions to which Africans have immigrated. All types of FGC involve cutting neural innervation to the vulva: the clitoris, labia majora and minora. Most types include excision of the clitoris. Since the tissue of the vulva is highly innervated by nerves and their endings, I postulate here that the brain and spinal cord will respond to FGC as it would to any loss of neural targets or inputs: by rearranging neural networks. This, in turn, would affect neural signaling to target structures and modify sensory perception. Most scientific investigations of FGC have focused on its reproductive consequences. To fully appreciate its effects on the lives of women, however, an understanding beyond the reproductive system is necessary. Exploring the potential neural changes of FGC may help explain the mixed responses of the women themselves and identify new directions for research to understand their lives. A neurobiological analysis may also help us understand how cultural practices inscribe meaning on central nervous system structures, affecting mind as well as body.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".