On Reducing the Sexual Assault of Women: What Can Economists Contribute to the Debate?
Bibliographic record
Abstract
In Canada, it is estimated that only about 5 percent of sexual assaults are reported to police and fewer than 1 percent of assaults result in convictions. The reasons for this are discussed in this commentary using results from a formal model in economic theory. In the model, if police overestimate the probability that women’s reports of assault are false, as the evidence clearly documents, they under-investigate. This in turn reduces the reporting of actual assaults and reduces the conviction rate. The attrition rate of active files (as women drop out as a result of the challenges within and outside the system) may further reinforce the incentive effects of police disbelief. These effects are compounded by the fact that, in common law, the Crown prosecutor represents not the victim but rather the society at large. Policy recommendations that stem from the model include an emphasis on victim advocates, who can increase police belief and hence spur police efforts and reduce attrition rates, leading to more reports and convictions and fewer assaults. In considering punishments for false reports, it is argued that due consideration must also be given to the effect such a punishment may have in reducing truthful reports and hence in increasing the number of assaults.
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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 | 0.025 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".