“It's not that serious”: Individual and situational factors that influence perceptions of online versus in-person sexual coercion
Bibliographic record
Abstract
The impact of technology-facilitated sexual violence (TFSV) is often minimized, especially when it is compared to in-person sexual violence. This study explored the effect of situational and individual factors on perceptions of a form of TFSV, sexting coercion, online and in-person. Undergraduates ( N = 1467, 72 % women) were randomly assigned to one of six conditions and read vignettes that manipulated the relationship (strangers, casually dating, committed relationship) and coercive tactics (threat, hint) used to pressure someone to sext. Overall, participants rated both online and in-person sexting coercion as unacceptable, suggesting most can recognize the inherent lack of consent and potential harm associated with such behaviours. Albeit with small to medium effect sizes, paired samples t -tests found that sexting pressure was viewed as less coercive when it occurred online compared to in-person. A regression showed that gender (women) and tactic (threat) were significant predictors of viewing in-person pressure to sext as more coercive. In contrast, higher Factor 1 psychopathic traits and participants in the hinting tactic condition viewed online pressure to sext as more acceptable, while men were more likely to rate online sexting pressure as acceptable and to perceive that the target had given consent. Education initiatives should consider these factors to counteract the downplaying of online sexual coercion and technology-facilitated sexual violence.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".