What's Love Got To Do With It? An Examination of Perceived Prosocial Motivations for the Perpetration of Intimate Partner Cyber Aggression
Bibliographic record
Abstract
Recent technological advancements have facilitated cyber relationships and in turn, cyber dating abuse has become more prevalent than ever. The present study examined cyber aggression perpetration in the form of excessive monitoring as a function of perceived prosocial motives. It was hypothesized that perpetrators reporting prosocial motivations would be significantly related to high levels of perpetration. Additionally, relationship investment was predicted to be a moderator of the relationship between perceived prosocial motives and perpetration. The third hypothesis was that gender would moderate the relationship, such that females would exhibit a stronger relationship than males. To examine these hypotheses, undergraduate university students (N = 513) completed questionnaires reporting the frequency and type of cyber dating abuse they perpetrated, the underlying motivations perpetrators reported lead to perpetration, and relationship investment. This research has relevant implications for the development of intervention and prevention programs aimed at reducing the occurrence of online dating 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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".