Bibliographic record
Abstract
In this article we attempt to identify the impact of social eects on the decision to practice excision on girls, based on the methodology used by Bertrand, Luttmer and Mallainathan (2000). We are particularly interested in social determinants, and make use of the heterogeneity of behaviors according to area of residence, ethnicity and religion. We focus on the interaction between the density and the quality of contacts to infer a social network. We use the percentage of individuals of the same ethnic group and religion, living in the same survey area, to measure the quantity of contacts, and the percentage of excised women of the same ethnic group and religion to measure the quality of contacts. To implement our trials, we use data from the Burkina Faso's Demographic and Health Surveys 2003, which supplies information on the prevalence of female genital mutilation (FGM) and on the characteristics of Burkina Fasan households. Our results show that social pressure is strongly correlated to the decision to practice excision in Burkina Faso households. Classi…cation JEL: I18; I19; I32; Z13
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".