Third Party Citizen Reactions to Interventions in a Genocide: The Influence of the Social Categorization of Victims and the Norm of a Responsibility to Protect
Bibliographic record
Abstract
Groups involved in ongoing genocidal conflicts within a nation may appeal to third nations, who are neither perpetrators nor victims, as potential allies.The present research investigated social psychological factors that influence citizens of a third party country (Canada) to support and participate in actions to intervene in an ongoing genocidal conflict in a distant region of the world (Darfur).Study 1 (n = 135) examined the influence of social categorization (decategorization, outgroup and common ingroup) on participants' endorsement of prosocial actions, as well as the mediating role of psychological antecedents, including perception of complicity in the genocide due to inaction, perception that the ingroup could be held accountable for their inaction, and guilt (for the situation of the victims and for the ingroup's inaction).The results suggested that framing the plight of victims of genocide in terms of the victimization of particular individuals might be more effective in eliciting appraisals and emotions that encourage support for intervention.Study 2 (n = 100) built on the results of Study 1 to explore whether the influence of recategorization of the victims as humans (relative to outgroup members) on endorsement of prosocial actions and its antecedents was moderated by individuals' evaluation of human beings (humanity-esteem).The moderation hypothesis was not supported; however, portraying the victims as a part of a common humanity was associated with greater feelings of guilt for the victims' plight.In addition, the more participants held humans in high esteem, the more likely they were to endorse government intervention.Study 3 (n = 321) assessed whether the relations among complicity, feelings of guilt and prosocial actions were altered when competing norms that prescribed action against genocide were made salient (i.e., the norm of a iii responsibility to protect versus the norm of non-intervention in a sovereign country).It appears that people were more likely to comply with norms that prescribed demanding actions, such as the protection of others, only when there were no alternative norms that provided an escape from such obligations.The implications of these findings for citizen support for international interventions in conflict regions are discussed.I would like to acknowledge the contribution of several individuals and organizations that have made the completion of this doctoral thesis possible.First, I would like to acknowledge the doctoral scholarships that were awarded to me by the Canadian Institutes of Health Research (CIHR; 200710CGD-188128-159950) and by Carleton University.I would also like to thank the participants in my studies for their time.A special thanks goes to member of my
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".