What leads victims to resist? Factors that influence victim resistance in sexual assaults
Bibliographic record
Abstract
Purpose Victim resistance has been shown to have an important impact on the outcome of sexual assaults. Thus, the factors that affect a victim’s likelihood of various levels of resistance are relevant to consider, given the possibly detrimental effect these actions can have on crime outcome. While not intended to blame the victim in any way, it is important to examine the role the victim plays within a sexually coercive interchange in order to completely understand the sex crime event and, thus, be able to inform potential victims as to the patterns that increase resistance and, potentially, overall violence. The paper aims to discuss this issue. Design/methodology/approach Sequential logistic regression analyses were conducted on a sample of 613 sex offenses (incorporating both adult and child victims) to examine the individual and combined effects of offender lifestyle, disinhibitors, victim vulnerability, situational impediments and offender modus operandi on victim resistance levels. Findings Results suggest that indicators of offender mindset are significant, particularly the use of pornography prior to the crime, and affect victim interpretation and response to the offender’s actions during the course of the assault. Other relevant factors include the victim’s age and the degree of violence present in the offender’s approach and subsequent offending strategies. Originality/value This information would be helpful to incorporate into victim education programs so that past and future potential victims can better understand the criminal event and the causes and effects of their own actions within that event.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".