Performing Veteranhood Through the Creative Arts: An Ethnographic Study of Recognition and Sociality Among Veterans Living in a Canadian Seniors’ Village
Bibliographic record
Abstract
This research explores the performance of veteranhood through the creative arts.It investigates how music-making affects sociality and recognition of Canadian World War II and Korean War veterans living in long-term care.I draw upon phenomenology as the guiding theoretical framework to explore the embodied dimensions of sociality, recognition, and performance of veteran identity through the creative arts.This dissertation draws upon other theorists beyond phenomenology to analyze the myriad dimensions of institutional veteran care.I make the following arguments in this dissertation: First, I argue that the veteran-residents were provided with care for their physical well-being, as well as care for their social identities as veterans.I contend that the creative arts programs were a form of milieu therapy, where daily music-making and singing war songs together was a means to restore, rehearse, and perform veteranhood.Second, I argue that people entering long-term care went through a process of un-making and remaking as they were transformed into residents, and then into veteran-residents.I argue that this experience was negotiated by performances of self-authoring and expressions of resistance.Third, I argue that the biomedical and creative arts doxas of care provided for the veterans were ambivalently-related to one another and that each was constitutive of a different sense of selfhood.I explore how porters mediated the milieus and accompanied residents on these existential shifts.Fourth, I argue that the group music programs, such as the resident bands, provided a stage for veteran-residents' new forms of sociality and recognition in a milieu framed as "play" with musical materials from which to construct their identities.Finally, I argue that an ethopolitics and a politics of recognition informed social conditions around performances of veteranhood.Dr. Fatemeh Mohammadi for fostering a positive and cooperative atmosphere in which each of us flourished.With eternal gratitude, I thank my mother for all that she has given me.Her brilliance has inspired me to climb to new academic heights and our dialogues at the dining room table, on road trips, and while gardening helped me sort out my thoughts while ascending the intellectual mountains.Thank you for the years of Suzuki music lessons, summer music institutes, and support in all my musical endeavours.These are at the core of my being.To my husband, Christian, thank you for your tireless support in all ways.Your delight in my achievements were motivation to forge ahead.Thank you for always getting tickets and for building us a beautiful home.To our son, Isaac, thank you for your excellent timing,
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".