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Adolescent Girls Offered Alternatives to Commercial Sexual Exploitation: A Case Study from the Philippines

2017· article· W7139081317 on OpenAlexaboutno aff
Christopher Bagley, Susan Madrid, Padam Simkhada, Kathleen P. King, Loretta Young

Bibliographic record

VenueJournal of Media Literacy Education · 2017
Typearticle
Language
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueRural areaGovernment (linguistics)Standard of livingDeveloping country

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.417
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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