Child Sexual Abuse Images Online: Confronting the Problem
Bibliographic record
Abstract
This Special Issue stems from the international symposium, “Child Sexual Abuse Images Online: Confronting the Problem—Research, Policy, Practice,” which was held at Ryerson University in June 2014 with funding from the Social Sciences and Humanities Research Council of Canada (SSHRC). The collection of papers in the Issue focus on child sexual abuse images online from the perspectives of children's mental health, child protection, and law enforcement. The symposium brought together local and international academics, policymakers, child advocates, practitioners, law enforcement officers, child welfare workers, and other key stakeholders who assume various roles and responsibilities in responding to child sexual abuse. Over two days we shared knowledge, experiences, and insights related to the role of technology in child sexual abuse; specifically the implications of child sexual abuse images online. Through presentations, panel discussions, and round-table working group discussions, participants examined and shared current knowledge about child sexual abuse images online, identified key priorities, and determined critical strategies and vital next steps. The five articles in this Special Issue represent those cross-sectoral contributions. We thank the Editors of Child & Youth Services, Dr. Kiaras Gharabaghi and Dr. Ben Anderson-Nathe, for inviting us to develop a Special Issue for this journal based on the presentations made at the 2014 CSAIO Symposium. We also thank our external reviewers through whom all articles in this Issue were subjected to a rigorous blind peer review.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".