Laying Down the Family Burden: A Cross-Cultural Analysis of Resilience in the Midst of Family Violence
Bibliographic record
Abstract
Questionnaire data from a cross-sectional study of a randomly selected sample of 5,149 middle-school students from four EU countries (Austria, Germany, Slovenia, and Spain) were used to explore the effects of family violence burden level, structural and procedural risk and protective factors, and personal characteristics on adolescents who are resilient to depression and aggression despite being exposed to domestic violence. Using logistic regression to identify resilience characteristics, our results indicate that structural risks like one's sex, migration experience, and socioeconomic status were not predictive of either family violence burden levels or resilience. Rather, nonresilience to family violence is derived from a combination of negative experiences with high levels of family violence in conjunction with inconsistent parenting, verbally aggressive teachers, alcohol and drug misuse and experiences of indirect aggression with peers. Overall, negative factors outweigh positive factors and play a greater role in determining the resilience level that a young person achieves.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".