“Our Laws Have Not Caught up with the Technology”: Understanding Challenges and Facilitators in Investigating and Prosecuting Child Sexual Abuse Materials in the United States
Bibliographic record
Abstract
With technological advances, the creation and distribution of child sexual abuse material (CSAM) has become one of the fastest growing illicit online industries in the United States. Perpetrators are becoming increasingly sophisticated and exploit cutting-edge technology, making it difficult for law enforcement to investigate and prosecute these crimes. There is limited research on best practices for investigating cases of CSAM. The aim of this research was to understand challenges and facilitators for investigating and prosecuting cases of CSAM as a foundation to develop best practices in this area. To meet these objectives, qualitative interviews and focus groups were conducted with participants throughout the western United States. Two major themes arose from this research: Theme 1: Challenges to investigating and prosecuting CSAM; and Theme 2: Facilitators to investigating and prosecuting CSAM. Within Theme 1, subthemes included technology and internet service providers, laws, lack of resources, and service provider mental health and well-being. Within Theme 2, subthemes included multidisciplinary teams and training. This research is a first step in understanding the experiences of law enforcement and prosecutors in addressing CSAM. Findings from this study can be used to support the development of best practices for those in the justice system investigating and prosecuting CSAM.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".