Adolescent Girls Offered Alternatives to Commercial Sexual Exploitation: A Case Study from the Philipines
Bibliographic record
Abstract
Background: Up to 2% of adolescents and young women are subjected to commercial sexual exploitation (CSE) in the Philippines, an economically poor country that earns considerable revenue from “sex tourists.” Earlier research, in the 1990s in Metro Manila, described the living conditions of adolescents whose CSE was influenced by family poverty, their so-called “sex work” becoming a major source of income for families left behind in rural and provincial areas of Luzon. Recent research (up to 2014) indicates that conditions for adolescents experiencing CSE have, if anything, worsened. Methods: Following the original study, the researchers were able to offer scholarships with funds from a Canadian charity, which enabled 84 girls to leave “sex work,” and return to high school. Results: Follow-up 18 years later showed that being able to return to normal life, was successful for at least 61 (73%) of the young women who researchers were able to trace. Conclusions: We advocate vigorous efforts to prevent the recruitment and trafficking of adolescents into commercial sexual exploitation, and extend our comments to recent Canadian policy initiatives for adolescents experiencing CSE, since our original study was based on a Canada-Philippines comparison. In advocating the ‘universal living wage’ solution for avoidance of CSE, we argue that demonstration projects such as this can be important exemplars for global policy development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".