Political tools or ‘supercrips’? Civilian interpretation of wounded veterans at the Canada army run
Bibliographic record
Abstract
The figure of the wounded veteran is a significant problem for the state as visual wounds make manifest the toll of war. If not appropriately mediated, veteran injury may communicate to the public that the cost of war is too high. The state and media organizations working on its behalf thus manage mediated representations in ways that rehabilitate the wounded veteran, communicating narratives of redemption, hope, and overcoming. Significant work has critiqued the political work of mediated depictions of wounded veteran-athletes at events like the Invictus Games and Paralympics, but little work has explored civilian responses. This paper explores civilian responses to the presence of wounded veterans at a military-themed amateur athletic event, the Canada Army Run, as determined through in-depth, semi-structured interviews with 40 event participants. Though wounded veteran-athletes comprise a tiny minority of Army Run participants, their presence captures the imagination of many civilian runners. Analysing civilian participants’ interpretation of wounded veteran-athletes allows for insight into the degree to which rehabilitative discourses dominating media depiction of wounded veterans at the Invictus Games and Paralympics has permeated the popular imaginary. I found that while veteran injury was often taken for granted as inevitable, veterans’ wounds generated significant support for servicepeople specifically, and to a lesser extent the Canadian Armed Forces at large. However, affects of sympathy and support did not preclude critique of veteran treatment by the state, demonstrating that even at explicitly pro-military events space for dissent is possible.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.038 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".