Collective Trauma and Political Activism: Learning from the Bereaved Families of Shot Down Ukrainian Airline Flight 752
Bibliographic record
Abstract
On January 8, 2020, Ukraine Airline Flight 752 was shot down minutes after taking off from Tehran, Iran, by an Iranian surface-to-air missile. This happened hours after Iran had launched missiles at US targets in Iraq. Amongst the 176 people killed in this tragedy, many of them children, were 85 Iranian-Canadians who had traveled to visit their relatives in Iran. Over the past two years, families of the victims have struggled to find some healing from this trauma amidst political pressures in their fight to get accountability, transparency and justice for their loved ones. This paper is an account of group therapy sessions conducted over two years with these bereaved families, mostly mothers. I provide a detailed account of how relational psychoanalytic principles were utilized to develop a group psychotherapy model for dealing with this novice complex situation where the trauma is as individual as it is collective. For these families, grief and trauma is complicated by the sociopolitical issues surrounding the incident, including lack of transparency, dishonesty, insensitivity and absence of any attempts at reparations or reconciliation by the Iranian government in the aftermath of this tragedy. I examine how these political moments has come to inform psychological challenges of the group, including their emotions, dreams and fantasies. I further elaborate on how these experiences have transformed these individuals into political activists as observed not only in their individual and collective identities but also in their actual participation in the political arena.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".