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Record W4238391585 · doi:10.32920/ryerson.14645385

A Qualitative Study on the Experience of Female Adolescents Sexually Exploited by Men Online

2021· preprint· en· W4238391585 on OpenAlexaff
Amelia Sloan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFeelingThe InternetThematic analysisQualitative researchPsychologyReproductive healthQuality (philosophy)Developmental psychologySocial psychologyMedicineSociologyPopulationComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

As the Internet plays an increasing role in lives of adolescents, there has been a rise in the number of youth solicited online by adult men for sexual purposes. As a result, thes e adolescentsface significant and unique physical, social, and psychological health risks, which are poorly understood. To date, there is limited qualitative literature that tells the story of Internet sexual exploitation as experienced by female adolescents, or the impact of it. Beginning with the perspectives of female adolescents, the purpose of this research is to investigate the complexities, impacts, and implications of Internet Sexual Exploitation. Using Interpretive Description and guided by Feminist Theory, retrospective chart reviews of five young women were conducted. Thematic findings included feelings, risk factors, grooming experiences, and contextual features. Understanding the experience of females involved in Internet Sexual Exploitation allows healthcare providers to develop and deliver high quality services tailored to their needs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.158
GPT teacher head0.468
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes1
Has abstractyes

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