Female genital mutilation/cutting in Senegal: Is the practice declining? Descriptive analysis of Demographic and Health Surveys, 2005–2017
Bibliographic record
Abstract
To achieve the Sustainable Development Goals, female genital mutilation/cutting (FGM/C) is one of the most prominent issues world leaders and governments must address. In Senegal, estimates from the 2017 Senegal Demographic and Health Survey show that almost a quarter of women aged 15–49 have undergone FGM/C, while 14 percent of girls aged 0–14 years have been cut. Given the many interventions that have been implemented in Senegal with the intention of scaling down FGM/C rates, the key question is: To what extent has the practice declined? The aim of this study, as presented in this working paper, was to generate evidence on where, when, and how FGM/C has been practiced in Senegal over the past 13 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".