Virtual and real paths : uma revisão sistemática sobre fatores de risco de reincidência criminal em ofensores MASC e mistos
Bibliographic record
Abstract
The present work is a systematic review aimed at identifying and comparing risk factors for criminal recidivism among child sexual exploitation material (CSEM) offenders and mixed offenders, as well as critically analyzing the main risk assessment instruments applied to these groups. Twenty-four articles, selected in accordance with the PRISMA protocol, were reviewed, ensuring methodological rigor and scientific quality. The research, predominantly originating from Canada, the United States, Australia, the United Kingdom, and Germany, reveals that fifteen studies compare risk profiles between CSEM and mixed offenders, eight focus on risk assessment instruments, and one deals exclusively with CSEM offenders. The samples are mostly comprised of adult males, with average ages between 30 and 40 years, highlighting demographic homogeneity and posing a significant limitation to the generalizability of the results. Findings show that, while CSEM offenders tend to present lower levels of antisocial behavior, greater relational/professional stability, and recidivism almost exclusively in the online sphere, mixed offenders exhibit more impulsivity, psychosocial pathology, and both digital and physical recidivism. The CPORT stands out as the reference instrument for CSEM offenders; for mixed offenders, it is recommended to combine structured tools (STATIC-99R, SVR-20, STABLE/ACUTE-2007) with digital assessment instruments. Limitations remain relating to the diversity of samples and the integration of dynamic psychosocial factors. This review underscores the need for differentiated, multidimensional, and culturally sensitive approaches to assessment and intervention, recommending the development of multicentre longitudinal studies and instruments adapted to the digital reality and the psychosocial complexities characteristic of these sexual offense profiles.
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 | 0.030 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".