Learning to Share Gendered Military Experiences: An Autoethnographic Exploration of Expressive Writing in Transforming Military Cultures
Bibliographic record
Abstract
This article discusses the analytic-evocative autoethnographic exploration of my learning experiences planning for, facilitating, participating in, and reflecting on a series of expressive writing workshops for women-identifying Canadians who have served in the military. I explore how the intersection of expressive writing, adult education, and feminist antimilitarism can inform understandings of individual military service, the collective institution of the military, and transforming military cultures. I detail my methodology of autoethnography, including a discussion of method and analysis, which demonstrates how the core elements of expressive writing and autoethnography are at odds with those of military cultures. For these reasons, expressive writing and autoethnography hold great potential for transforming military cultures, which is why I chose to combine these creative forms of writing and exploration in my research. I explain my thematic findings of: reclaiming military identity, memories, and story through imagination; and, building community through story-telling inspired by shared gendered military experiences. I present a found poem created from my writing during the workshop series that emerged from my thematic findings. I conclude with implications for adult education and autoethnographers in the context of working toward transforming military cultures.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".