Reconceptualizing Urban Warfare In Canada: Exploring the Relationship between Trauma, Post-traumatic Stress, And Violence Among Male Combat Soldiers and 'Street Soldiers'
Bibliographic record
Abstract
When we hear the word “trauma” we often conflate this term with the experiences of combat soldiers returning from war. While the current state of the art on trauma/Dissociation and PTSD has predominantly focused on the traumatic experiences of combat soldiers, less is known about other vulnerable populations who are exposed to similar types of war-related violence, including gang members (or street soldiers). Drawing on a multiple-case study approach, including, in-depth qualitative interviews, my study compared and contrasted the experiences/stories of combat veterans with those of ex-gang members as a way to understand the psychological sequelae of “gang violence”. Building on the knowledge on combat trauma, I sought to develop a Trauma-Based Theory of Gang Violence that identifies how pre-existing (i.e. poverty, masculinity and exposure to “pre-war” violence), peri-traumatic (i.e. indoctrination and exposure to violence) and post-traumatic factors (i.e. transition stress, post-traumatic stress and the reenactment of trauma) may contribute to gang membership and the cycle of violence that permeates marginalized/gang-dominated communities.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".