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Record W4362512807 · doi:10.22215/etd/2023-15349

(Re)Producing military mythology at the Canada Army Run

2023· dissertation· en· W4362512807 on OpenAlexaffabout
Bridgette Desjardins

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsCarleton University
Fundersnot available
KeywordsMythologyMilitary theoryPoliticsMilitary psychologyMilitarismMilitary sciencePolitical sciencePublic relationsLawSociologyHistory

Abstract

fetched live from OpenAlex

This thesis offers a novel exploration of militarism in amateur sport, wherein civilians are participants rather than spectators, thus allowing the body to be centered when exploring reproductions of militarism.I analyze the 2019 Canada Army Run -an annual road race organized and hosted by the Canadian Armed Forces -to explore how embodied interaction with the pro-military messaging saturating the event affects participants' political orientation to the Canadian military.I draw on ethnographic data gathered while running in the 5k race and attending pre-and post-race activities, as well as 40 semi-structured qualitative interviews with race participants and three interviews with event organizers.Theoretically, I utilize Barthes' (1972) work on mythology to conceptualize the ideological significance and political affects of military myth and explore the ways myth is circulated and produced at the Army Run.The most predominant Canadian Armed Forces myths described by participants are: having a standing, armed military is necessary and inevitable; the military is moral, only using force when necessary and primarily engaging in aid work; and the military is misunderstood by most civilians and as a result is under appreciated.These myths circulate at the Army Run in myriad ways.The opportunity to build civilian-military connections via interpersonal interaction was the event's most effective tactic in generating civilian support.The race expo facilitated interaction between civilian race attendees and military servicepeople who volunteered for or participated in the races, while featuring individual deceased veterans along the racecourse encouraged participants to see the military not as a faceless institution, but rather as the sum of its parts: individual servicepeople.Army Run participation notably impacted the orientation of civilian participants who had no strong attachment to the military prior to participating.For this group, participation inspired increased political support for the military, largely as a result of developing a perceived connection to the military.Ultimately, I argue that the Canada Army Run (re)produces myths of military necessity, morality, and under appreciation via participants' embodied engagement in military themed spaces and with military members.Through physical engagement in the Army Run's militarysaturated event space and interaction with military servicepeople, participants come to feel connected to the military, and it is through this felt connection that increased support is generated and attention is shifted away from military politics and practices and toward the interpersonal.The Army Run's presentation of a depoliticized, individualized Canadian Armed Forces allows for increased public support that remains constant as it is not grounded in the reality of military action, thus enabling the proliferation of military power and investment.My journey through this doctoral degree was an odyssey of unimaginable proportion.I'm uncertain whether the global pandemic or moving to Australia disturbed my academic progress more, which, I believe, says something about the degree of chaos both changes inspired.I have many people to thank for helping me drag my poor, depleted brain to the finish line.First, thanks to my interview participants for their generosity in sharing their time and experiences with me.Without you, I'd have no data to analyse and thus no thesis to write.I very literally could not have done it without you all.But, beyond providing me with data to write about, these interviews moved me in unexpected and often challenging ways that caused me to grow not just as a scholar but also as a person.For that, I thank you all.I'd also like to thank the Social Sciences and Humanities Research Council of Canada for funding this research.Similarly, thanks to the Department of Law and Legal Studies and all the wonderful staff that keep (or kept) the department running amongst significant challenges, especially Andrew and Barb.I found it takes a village to raise a burgeoning academic to the level needed to write a successful thesis, and so I'd like to thank my academic village.To the many faculty who shaped and/or inspired me at various points over the years -Doris Buss, Jane Dickson, Stacy Douglas, Meg Gaucher, Sheryl Hamilton, Phil Kaisary, Ummni Khan, and Dawn Moore -thank you for your part in creating such a rich tapestry of scholarly thought in the Legal Studies department.Thanks also to Fiona McLachlan, Brent McDonald, Ramón Spaaij and others at Victoria University's Sport and Social Change research lab for adopting me temporarily after I'd moved to Melbourne.Most of all, thank you to my committee.My external examiners -Erica Fraser and John Kelly -gave significant food for thought as I move forward, and getting the chance to meet and learn from John, the sport militarism scholar whose work has influenced me most, was very exciting.My committee members, Christiane Wilke and Mike Christensen, were wonderful mentors as I moved along this project and I appreciate all your time and patient advice.My supervisor, Dale Spencer, has been an absolute superstar.Despite being perhaps the busiest man alive, you always gave me the guidance I needed, for the thesis and the many and varied other projects I embarked on.You've always believed in me as a scholar and cared for me as a person and I cannot express how much that has mattered to me over the last five years.I am indebted to the PhD students who

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.391

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0250.029
Scholarly communication0.0090.003
Open science0.0020.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.028
GPT teacher head0.317
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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