Young Black Men's Embodied Experiences of Traumatic Gun Violence
Bibliographic record
Abstract
This thesis examines the experiences of 10 young Black men who have been impacted by traumatic gun violence either directly through being shot or indirectly by witnessing gun violence in their communities or losing loved ones to gun violence. Few studies have considered the traumatic and embodied impact of gun violence on young Black men. Using trauma theory and embodiment as theoretical frameworks, this research demonstrates how due to the lack of resources and recognition of young Black men’s experiences of traumatic gun violence, young Black men begin to “embody” experiences of traumatic gun violence physically and emotionally. Young Black men’s experiences of gun violence within the city of Toronto continue to be stigmatized, criminalized, and disregarded as a traumatic issue and the main approaches to solving gun violence have focused on criminal justice interventions and policing which do not engage with the actual “embodied trauma” of gun violence. This thesis demonstrates that to understand how young Black men embody traumatic violence we first need to understand how systemic violence and structural oppression, as expressed through racial profiling, over-surveillance, incarceration, police brutality, and experiences of poverty, coincide with the traumatic impact of gun violence, and directly relate to young Black men’s experiences of ongoing exposure to gun violence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".