Emotion regulation and attitudes toward FARC-EP ex-combatants and Venezuelan migrants: effects of a reappraisal training
Bibliographic record
Abstract
Colombia faces the dual challenge of integrating into civil life two large segments of population; more than fourteen thousand FARC-EP ex-combatants, as part of the peace agreement to end the five-decade conflict between that armed group and the Colombian Government, and nearly two million Venezuelan migrants. Successful integration heavily depends on fostering public acceptance of these groups. Prior research by Halperin et al. (Psychol Sci 24:106-11, 2013) and Hurtado-Parrado et al. (Front Psychol 10: 1-9, 2019) demonstrated the effectiveness of reappraisal training, a brief emotion-regulation intervention, in reducing negative emotions (e.g., anger, irritability, fear) and aggressive attitudes (e.g., support for war or opposition to the peace process), while increasing conciliatory attitudes (e.g., support for humanitarian aid). The present study extended those findings via testing reappraisal training to promote positive attitudes towards FARC-EP ex-combatants (Experiment 1) and Venezuelan migrants (Experiment 2). In both experiments, reappraisal training reduced negative emotions and support for aggressive statements, while increasing support for conciliatory statements. In addition, negative emotions mediated the effect of reappraisal on both aggressive and conciliatory statements. Lastly, reappraisal training increased participants' willingness to donate, a measure of prosocial behavior tested for the first time in this line of research. These findings add to the evidence of the effectiveness and generalizability of reappraisal training across a wider range of social targets and prosocial behaviors, and its potential to inform public policy and promote larger-scale social integration efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".