Author's Response * Female sex worker typology: too complicated to be used pragmatically
Bibliographic record
Abstract
We appreciate Haldar's interest in our paper Devising a female sex work typology using data from Karnataka, India published in the IJE. We would like to clarify a few points in response to his letter. In our paper we classified female sex workers (FSWs) based on their reported main place of solicitation and main place of sex, and assessed which criterion best captured the variation in HIV risk observed among the sample studied. Our analysis suggests that in Karnataka state, classifying FSWs based on both the main place of solicitation and the main place of sex can best identify FSWs at high risk, and hence proposed that this typology is employed in HIV targeted interventions. Our paper is based on secondary analysis of baseline data from the Integrated Biological and Behavioral Assessment (IBBA) surveys conducted among FSWs in Karnataka state with the primary purpose of measuring various programme outcomes. Haldar is correct to point out that the sample size statement we included was that which determined the size of the IBBA survey for its primary purpose. The sample size (over 2000 participants) is nevertheless substantial, has clearly allowed important differences and associations to be detected, and the precision of our findings is clear from the confidence intervals presented in Tables 4 and 5 in our paper.
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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 | 0.025 | 0.271 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".