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Record W2898200321 · doi:10.1111/cars.12223

Professionals’ Perspectives on Viewing Child Sexual Abuse Images to Improve Response to Victims

2018· article· en· W2898200321 on OpenAlexafffund
Andrea Slane, Jennifer Martin, Jonah Rimer, Angela W. Eke, Roberta Sinclair, Grant Charles, Ethel Quayle

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2018
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsGovernment of OntarioUniversity of British ColumbiaToronto Metropolitan UniversityRoyal Canadian Mounted PoliceOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsChild sexual abuseSexual abusePsychologyChild abuseLaw enforcementPhenomenonMental healthHealth professionalsChild protectionDisciplineMedical educationMedicinePoison controlSuicide preventionPsychiatryNursingMedical emergencyPolitical scienceHealth care

Abstract

fetched live from OpenAlex

The complexity of the phenomenon of child sexual abuse images online (CSAIO) benefits from cross-disciplinary collaboration across law enforcement, child protection, and children's mental health. Through focus groups with professionals working in these fields, this article focuses on when and whether professionals who work with child sexual abuse cases should be exposed to viewing CSAIO and if so under what circumstances doing so would benefit investigations and support services for victims. In a broader sense, this article is about professional experience, decision making, training, and collaboration around a particularly difficult professional experience, namely exposure to viewing CSAIO.

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)0.007
metaresearch head score (Gemma)0.018
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 score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.016
Scholarly communication0.0070.004
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.036
GPT teacher head0.329
Teacher spread0.293 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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Same venueCanadian Review of Sociology/Revue canadienne de sociologieSame topicChild Abuse and TraumaFrench-language works237,207