Bibliographic record
Abstract
The following explores Post Traumatic Stress Disorder (PTSD) as a social problem by applying Van Gennep’s “rites of passage” to the homecoming experiences of (mostly) American veterans of modern wars and peacekeeping missions. It is my intention to suggest that the absence of a socially defined and publicly acknowledged period of aggregation for homecoming veterans exacerbates and lengthens the transitional adjustment period between separation from the field and reincorporation into civilian life; and in the most complex cases, prevents veterans from reincorporating into civilian society at all. Additionally, Turner, Douglas and Bloc’s ideas regarding liminality, pollution and infamous occupations are used to address the marginalization of veterans returning from missions. The bulk of PTSD information is drawn from accounts of American veterans’ experiences; however, brief accounts of experiences of Israeli and Dutch veterans have been included for comparison. According to Young, the American Psychiatry Association (APA) defines PTSD as resulting from, “an event outside the range of usual human experience that would be markedly distressing to almost everyone”. Furthermore, such an event usually involves some kind of extreme violence (caused by human beings or natural disaster). Litz, Gray and Bolton suggest that a traumatic event may also involve “intense or protracted exposure” to adverse, unpredictable and uncontrollable experiences that challenge
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".