“Once you see it you can't unsee it”: Law enforcement trauma and immersion in child sexual abuse material
Bibliographic record
Abstract
Police working with child sexual abuse material (CSAM) have a complex and significant job. Their experiences have potential to cause trauma, and so to inform better responses and supports, more must be understood about their work from their perspectives. This article focuses on internet child exploitation law enforcement (ICE LE) experiences and perspectives regarding the impacts of working with CSAM, trauma, and implications for professional practice. The sample encompassed 27 ICE LE investigators and supervisors in Ontario, Canada whose main job is investigations involving CSAM. Three focus groups were conducted with participants, followed by an inductive thematic analysis, whereby themes were developed through a multi-stage process of coding and clustering. Instead of simply viewing, seeing, hearing, being exposed to, or working with CSAM, participants described immersion in/with the material. Framed within a taxonomy of trauma focusing on events, experiences, and effects, participants described being warned about the depravity and difficulty of CSAM, continually seeing and hearing distressing content, working closely with CSAM, and being greatly impacted by audio. They reported effects including shock, never forgetting CSAM, feeling suspicious, and wanting distance. Events, experiences, and effects were recounted as experiential and detrimental. Therefore, it is more accurate to categorise participants’ immersion in/with CSAM as a direct experience of primary trauma, not “secondary” or “vicarious” trauma. Implications for multiple sectors involved in child protection and practice are discussed. • Reports the experiences of police immersed in child sexual abuse material (CSAM). • Employs a holistic trauma framework focused on events, experiences, and effects. • Argues participants experience primary trauma and not “secondary” or “vicarious.”. • Suggests how to better support professionals working with CSAM. • Provides learnings for sectors likely to work with CSAM and experience trauma.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".