Demystifying trauma in international relations theory: From incomprehensibility to the liberatory <i>real</i>
Bibliographic record
Abstract
Abstract Recent work on trauma and memory in international relations has sought to emphasize the key role trauma plays in state and community formation, security policies, the mediatization of atrocities, and transitional and social justice. This article problematizes the doxology of trauma in this body of work: the assumptions about the traumatic that go without saying because they come without saying in the discipline. We counter, in particular, international relations’ unreflective consumption of Cathy Caruth’s paradigm of trauma as an incomprehensible shock. In this article, we excavate the contours, origins, and effects of this doxology. We first use the examples of post-conflict struggles for truth and reconciliation and the COVID-19 pandemic to illustrate that international relations’ vision of trauma centralizes a psychiatric and medicalized paradigm of governance and management that depoliticizes suffering. We then seek to provide an alternative account of trauma woven in dialogue with the psychoanalytical reflections of Francophone and Lusophone scholars in the Black Radical Tradition, particularly Fanon, Mbembe, Kilomba, Nascimento and Gonzalez. The goal is to move from a theory of trauma-as-event to an understanding of (colonial/racial) trauma as it appears in the writings of those who never felt protected or at peace in the white colonial order.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.106 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".