Navigating Cyber Intimate Partner Violence and Conflict: Negative Anticipation and Emotions During Text-Based Versus Face-to-Face Conflict Discussions in Young Adult Couples
Bibliographic record
Abstract
Young adult couples frequently use text messages to discuss conflicts within their relationship. While face-to-face conflicts have been shown to elicit more negative anticipation and negative emotions in victims of traditional, offline forms of intimate partner violence (IPV) (e.g., psychological and physical) compared with nonvictims, no study has examined how victims of cyber IPV (C-IPV) experience conflicts, either text-based or face-to-face. This study investigated, among young adult couples, the interplay between C-IPV and conflict modality (text-based vs. face-to-face) in association with negative anticipation and negative emotions during the discussion. A community sample of 102 young adult couples completed a self-reported questionnaire of C-IPV in the last six months and engaged in two conflictual interactions: one text-based and one face-to-face. Negative anticipation of the upcoming discussion was assessed prior to each interaction, and negative emotions were assessed immediately after. Results suggest that text-based conflicts were associated with higher negative anticipation in partners experiencing average or high levels of C-IPV. In turn, negative anticipation was linked with higher negative emotions. Findings highlight the importance of promoting healthy conflict management through technology-mediated communication, especially among young adult couples experiencing C-IPV.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".