Bibliographic record
Abstract
This article was named the winner of the 2019 Enloe Award. The committee commented: This article, which draws upon Enloe’s central ideas on militarized masculinities and enriches/refines the literature by focusing on the “helpful hero” trope utilized to justify the Canadian military intervention in Afghanistan in 2001–2014, is innovative in its framing and methods. The author’s discourse analysis of “helpful heroes” and her broader analysis of “helpfulness” opens up new pathways for thinking about militarized masculinities in less binary terms, and for theorizing the deeply embedded, intertwined experiences of militarization and humanitarianism, particularly the white, Eurocentric savior complex in international conflicts. It is a very interesting article for thinking about how justificatory tropes of heroism obscure the practice of violence.ABSTRACTThis article discusses how models of militarized masculinity can be mobilized to justify the use of violence internationally. I first trace a model of militarized masculinity – the “helpful hero” – that emerged during the Canadian military intervention in Afghanistan in 2001–2014. I demonstrate that this ideal type emerged from competing preferences for “warrior” or “peacekeeper” identities of the Canadian Armed Forces (CAF). The helpful hero model functioned to legitimize participation by the CAF in the Afghanistan War due to the positioning of soldiers as helpful, rather than as overtly aggressive or militaristic. I argue that the helpful hero ideal was not simply another ideal type of militarized masculinity, but also a symbolic trope that functioned politically to erase and obscure actual military violence. The article concludes by problematizing this model of militarized masculinity and noting the violent and imperial effects of war that cannot be seen when we imagine the military according to gendered mythologies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".