Sex-workers in a Country of Largest Muslim Population
Bibliographic record
Abstract
Indonesia is the largest Islam country in the world that has the biggest number of Muslims among other countries in the world. Hence this is the reason of the local governments, the governor of East Java Province and the mayor of Surabaya, to close the red-light areas. The purpose of this research is to know how the women became sex-workers in the biggest number of Muslims in the world. We observed and interviewed 20 informants of sex workers and former sex-workers; the Head of Local Health Center, NGO in those areas, and midwives. The data that had been collected were classified into themes. While being analyzed, the report was written and being classified. The results show that although the nation vows that every individual in this country has a religion, and mostly are Muslims, but prostitution is difficult to inhibit. We found many reasons that have made the women fell into the world of prostitution in this area of East Java. After the closing of the red-areas we found that they have secretly continue doing their job because they do not have other skills that can make as much as money that they earned as sex-workers. We conclude that the purpose to preserve a good religious environment by closing the red-areas cannot be realized. The closing of the red-areas has brought new problems because the spread of HIV/AID and sexually transmitted disease are more difficult to control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".