Agency, Responsibility, and Civic Engagement: Experiences of Former Air Cadets of Diverse Identities
Bibliographic record
Abstract
This dissertation examines the experiences of air cadets from the Greater Toronto Area. It inquires how young people of diverse identities experienced the program in relation to agency, responsibility, and civic engagement. The interpretive qualitative study gathered stories about participants’ experiences in air cadets by drawing on aspects of narrative inquiry. Data was collected via a journal and photovoice activity as well as two sets of interviews: one in-situ where participants enrolled in the program, and one online. More than 100 potential participants completed a screening tool that helped identify 17 diverse participants for in-depth study. Through analysis of participants’ stories, the author presents the meanings participants attribute to their experiences in relation to agency, responsibility, and civic engagement. The study offers a comprehensive and nuanced understanding of how young people grappled with issues of representation and inclusion in a formal citizenship education program. The author articulates how participants perceived the connection between their experiences in the air cadet program and their present-day embodiment of civic engagement. The structured hierarchical environment of air cadets produced both oppressive and liberatory conditions where agency manifested explicitly and publicly as well as subtly and privately, further complicating understandings of agency. Significant findings reveal how Black participants expressed a need for representation at the adult leadership level to better support their participation in the program, and how women and non-binary participants described feeling a sense of belonging, but with nuance. Across the dataset, a deep sense of connection to the air cadet community was identified as a key component of participants’ experiences, which contributed to an internalized sense of responsibility. The findings also indicate that as adults, participants were able to draw on skills and dispositions they learned in air cadets, even in cases where participants felt their understandings of good citizenship in adulthood drastically differed from what they perceived the program espoused. The dissertation concludes by outlining six strategies for program designers and educators to use while developing and implementing programs in their communities that support agency, responsibility, and civic engagement in ways attuned to the needs of diverse young people.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".