Police classification of sexual assault cases as unfounded: an exploratory study
Bibliographic record
Abstract
The General Social Survey (GSS) on Victimization indicates that the non-reporting rate in sexual assault cases, already significantly higher than for other violent offences, rose from 78% in 1999 to 88% in 2004 (Statistics Canada, 1999, 2004). This research addresses one of the considerations thought to contribute to high non-reporting rates: the high proportion of sexual assault allegations that are classified as “unfounded” by police, compared to other violent offences.\nIn fact, the rate of sexual assault cases deemed to be unfounded – that is, not to have occurred or not to have been attempted – has been shown to be twice as high as for other violent offences, and this difference has been sustained over several decades (Kong et al, 2003). Further, rates of unfounded classifications have been shown to vary widely from jurisdiction to jurisdiction.\nAny erroneous classification of sexual assault cases as unfounded has serious implications for reported crime statistics since, once determined to be unfounded, these cases are not included in numbers at either the local or the national level. In addition, there can be serious implications for victims’ access to justice and support services as well as their safety and the prevention of further violence, since complainants that have been determined not to be victims would be unlikely to be referred to victim support services.\nGiven the above, high unfounded rates have long been a source of tension between police and individual victims, as well as between police and the wider women- and victim-serving community. Understandably, erroneous unfounded classifications can contribute to an erosion of women’s confidence in the justice system which, in turn, can lead to their reluctance to report sexual assault to the police.\nThis research was undertaken for the Justice Institute of British Columbia, with funding from the Policy Centre for Victim Issues and the Research and Statistics Division at the Department of Justice Canada, the BC Ministry of Community Services, and the BC Ministry of Public Safety and Solicitor General.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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 teacher head, 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".