Where the Political and the Psychological Meet: Moral Disruption and Children’s Understanding of War
Bibliographic record
Abstract
I argue for the inclusion of a moral dimension in the study of children’s responses to loss and trauma in contexts of armed conflict and political violence. In my clinical and research studies, the disruption of a rule-governed moral universe is revealed in the symbolic and verbal representations of both community-based and clinically referred children whose parents have disappeared or been killed in contexts of political violence. Dilemmas related to good and evil, trust and betrayal, protection and aggression, are prevalent in war-affected children’s representations of their traumatic experiences. Not only should more attention be paid to children’s construction of moral narrative in the aftermath of political violence, but also this should be done in conjunction with an examination of the moral narratives embedded in national and cultural ideologies. Following political violence, the child’s search for meaning occurs at the same time that his or her relevant culture(s) or nation(s) are struggling to construct a collective narrative, often in the context of conflicting historical accounts, memories and narratives. Interventions and research addressing the recovery of war-affected children should recognize the interconnectedness of political, social, psychological and moral dimensions of armed conflict and political violence.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| 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".