Beyond “PTSD”: How stories and artworks that “make strange” can serve as signposts on new maps toward the communalization of military trauma
Bibliographic record
Abstract
The psychiatric system, in large part due to its reliance on the Diagnostic and Statistical Manual (DSM), has a tendency to pathologize ordinary human reactions to difficult life events, and to individualize treatments for “mental illness.” This article builds on existing literature that is critical of psychiatry and proposes that art and stories that ‘make strange’ and elude easy interpretation may serve as a powerful counterpoint or complement to the ‘standard way of doing things’ when it comes to mental health care. Using military trauma as an example, this article highlights the inadequacies of Post Traumatic Stress Disorder (PTSD) as a diagnostic category; and, drawing from critical literature in the field and the author’s own experiences working with groups of traumatized veterans, it illustrates how and why ancient mythology and modern art especially may serve as useful tools for those who are having problems with living. The ‘disorienting dilemmas’ and consciousness-raising discussions such works evoke have the potential to touch on vital, nuanced, and philosophical aspects of trauma and suffering that are too often overlooked by the psychiatric profession.Keywords: military trauma; mental health; modern art; theatre; ancient mythology; transformative learning; museums; PTSD.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.061 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".