Cyber-Sexual Violence And Negative Emotional States Among Women In A Canadian University
Bibliographic record
Abstract
<em>Cyber-sexual violence refers to a form of harmful sexually aggressive behaviors committed with the facilitation of digital technologies. Such harmful behaviors can include non-consensual pornography and other image-based sexual exploitation, online sexual harassment, cyber-stalking, online gender-based hate speech, and the use of a carriage service to arrange/attempt to arrange a victim</em><em>’</em><em>s sexual assault. This article examines the cyber-sexual violence experiences reported by a sample of women on university campuses in </em><em>Ontario</em><em>, </em><em>Canada</em><em>. Specifically, this study documented the types and forms of cyber-sexual violence that female university students have experienced, whether they disclosed the incidents and their association with negative health emotional states. This study provided evidence indicating that experiences of cyber-sexual violence are associated with symptoms of depression, anxiety, stress, and posttraumatic reactions, regardless of individuals</em><em>’</em><em> disclosure experiences. In light of these findings it is crucial that service providers and legislative initiative begin to adapt to the changing technological nature of crimes against women.</em>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".