Strangers Versus Non-Strangers: The Nature of Police-Reported Sexual Assault Characteristics
Bibliographic record
Abstract
Sexual assault is a prevalent crime in our society with approximately 21,500 cases of sexual assaults reported in Canada (Statistics Canada, 2015). The literature that examines the perpetrators of sexual assault has shown marked distinctions between those who were strangers and those who were known to their victims, in terms of the severity and nature of the violence used and the characteristics of the perpetrators. Many studies have been conducted using victim surveys, while fewer studies have examined police-reported sexual assaults using local police data. The current study examines a sample of 697 police-reported sexual assault cases. Stranger- and non-stranger-perpetrated sexual assaults were compared on demographic characteristics of the perpetrators and the victims, such as age and ethnicity, and the criminal history and recidivism rates of perpetrators. Our results indicate that there were many similarities, which suggest homogeneity between the two groups. However, some notable differences were also found. Specifically, stranger perpetrated sexual assaults were more often reported downtown rather than areas outside of the downtown core and during warmer months of the year. A greater proportion of stranger perpetrators had prior criminal histories and prior sexual offending histories compared to non-stranger perpetrators. Also, a greater proportion of stranger perpetrators committed recidivistic acts than non-stranger perpetrators. The findings have implications for policing in prioritizing cases and allocating resources, particularly in light of the potentially greater risk that stranger-perpetrators pose in further committing criminal acts. The use of evidence-based practices will be highlighted in this poster presentation. Discipline: Psychology Faculty Mentor: Dr. Sandy Jung
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".