Examining how bystanders intervene and perpetrators respond to intervention during experiences of sexual aggression
Bibliographic record
Abstract
Despite increasing uptake of bystander intervention programs to prevent sexual aggression, rates of sexual violence have remained persistently high. Those who witness sexual aggression among their peers can provide another vantage point regarding the strategies that perpetrators use and valuable information about ways in which perpetrators divert bystanders’ intervention—all information that can inform prevention programs. Participants ( N = 247) completed structured and open-ended items about occasions they had witnessed that involved efforts to force sex on a non-consenting individual. Reports were content coded for strategies leading to sexual aggression for 99 participants who had witnessed a recent alleged act of sexual aggression. Most (93%) reported perpetrators’ use of early physical pressure (e.g., unwanted grinding, following, isolating, violating personal space, pulling, blocking others) that typically escalated into more overt physical pressure and force. Verbal coercion (e.g., arguing, insisting, begging) was witnessed by 40% of participants, and 14% of participants reported witnessing the target being pressured to consume excessive levels of alcohol. Coded themes captured perpetrators’ defensive interactions with concerned bystanders, such as making excuses, minimizing their intentions, feigning innocence, and using humour to divert attention from sexually aggressive efforts. Results have implications for prevention efforts incorporating bystanders as well as education about the risk of assault.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".