Assessing the risk of users of child sexual exploitation material committing further offences: a scoping review
Bibliographic record
Abstract
The aims of this scoping review were to determine: how practitioners assessed the risk from CSEM users; the tools commonly used; the populations these tools were designed for and the evidence-base for them. Digital databases and websites were examined to identify sources published in English from January 2000 onwards. In total 36 studies were identified conducted in the UK, Canada, the Netherlands, the USA, Australia, New Zealand, and Spain. Few studies examined or evaluated risk assessment practice. Most tools have not been validated with CSEM users, nor females, ethnic minority populations, or individuals with Autism, mental health conditions or disabilities. Although some tools could be used cautiously, with men convicted of both CSEM and contact sexual offences, they should be used only to rank individuals, as normed probabilities are not applicable. These findings pose challenges for professionals; work is urgently needed to develop and appropriately validate tools for this population.Practice impact statement This scoping review provides practitioners internationally, in particular those responsible for the risk assessment or case prioritisation of CSEM users, with an overview of the international, English language, evidence-base for risk assessment tools and processes for CSEM users. It should assist practitioners in the identification of the tools and processes that are most appropriate for their work and provide information that will be useful in reports, e.g. to justify the use of the tool(s) and note the limitations. Since the review highlights the urgent need for the development of and validation of tools, particularly those with dynamic variables, it is also important for practitioners, managers, and researchers responsible for developing tools and processes in this area.
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 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 | 0.047 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".