Youth’s Experiences of Cyber Violence in Intimate Relationships: A Matter of Love and Trust
Bibliographic record
Abstract
Adolescents and young adults are particularly at risk of experiencing or perpetrating cyber dating violence (CDV) in their romantic relationships. However, it remains difficult to understand the motivations behind tolerating or perpetrating CDV in romantic relationships. Romantic attachment and beliefs may be an interesting avenue to explore among youth victims of CDV. The current study aims to 1) document the association between romantic attachment and CDV victimization and perpetration while controlling for age, gender and other forms of DV, and 2) explore beliefs youth attribute to the use of technology in their romantic relationships. A total of 332 self-identified heterosexual youth, with an age range of 14 to 25 years old, completed a survey. A subgroup of 14 youth who reported experiencing cyber dating violence also participated in a semi-structured interview. Logistic regressions were used to assess the contribution of romantic attachment (anxious and avoidant) to each form of CDV (stalking, psychological and sexual CDV) followed by a thematic analysis exploring beliefs about the use of technology in romantic relationships. Anxious attachment significantly predicted psychological CDV as well as stalking victimization and perpetration. Avoidant attachment significantly predicted psychological CDV victimization and perpetration as well as sexual CDV victimization. In their narratives, youth reported that controlling, monitoring, harassing behaviors, especially credentials sharing, constitute proof of love and trust rather than manifestations of CDV. The results support the relevance of developing tailored interventions based on attachment and romantic beliefs, which appears to be a promising avenue for preventing various forms of DV.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".