“It’s a Feeling That Makes You Do Anything”: Youth Narratives of Love and Experiences of Victimization in Their Romantic Relationships
Bibliographic record
Abstract
Youth narratives of love are shaped by romantic experiences through observation of others’ romantic relationships, and by media commonly conveying romantic beliefs. Since past reports have linked romantic beliefs to dating violence (DV), studies need to explore narratives of love by youth who report DV victimization experiences to identify specific targets to address in DV prevention programs. This qualitative study explored the narratives of love by heterosexual youth and documented specific features according to their DV victimization experiences. Directed content analysis guided the analyses of semi-structured interviews of 82 participants aged 15 to 24 years ( M = 19.4; SD = 2.1). Most participants were cisgender females (75.6%) born in Canada to Canadian-born parents (54.6%). Four polarized narratives of love emerged: (1) Growing love versus love at first sight, (2) Completive versus fusional love, (3) Lucid versus triumphant love, and (4) Ongoing versus eternal love. Both participants who reported experiencing DV victimization and those who did not expressed non-romantic and romantic beliefs, although they used different wording to convey similar beliefs in their narratives. These findings underscore the importance of challenging the dominant romantic beliefs that may place youth at risk of experiencing DV and therefore, contribute to DV prevention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".