Political Violence and Adolescent Out‐group Attitudes and Prosocial Behaviors: Implications for Positive Inter‐group Relations
Bibliographic record
Abstract
Abstract The negative impact of political violence on adolescent adjustment is well established. Less is known about factors that affect adolescents' positive outcomes in ethnically divided societies, especially influences on prosocial behaviors toward the out‐group, which may promote constructive relations. For example, understanding how inter‐group experiences and attitudes motivate out‐group helping may foster inter‐group co‐operation and help to consolidate peace. The current study investigated adolescents' overall and out‐group prosocial behaviors across two time points in Belfast, Northern Ireland (N = 714 dyads; 49% male; Time 1: M = 14.7, SD = 2.0, years old). Controlling for Time 1 prosocial behaviors, age, and gender, multi‐variate structural equation modeling showed that experience with inter‐group sectarian threat predicted fewer out‐group prosocial behaviors at Time 2 at the trend level. On the other hand, greater experience of intra‐group non‐sectarian threat at Time 1 predicted more overall and out‐group prosocial behaviors at Time 2. Moreover, positive out‐group attitudes strengthened the link between intra‐group threat and out‐group prosocial behaviors one year later. Finally, experience with intra‐group non‐sectarian threat and out‐group prosocial behaviors at Time 1 was related to more positive out‐group attitudes at Time 2. The implications for youth development and inter‐group relations in post‐accord societies are discussed.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".