Researching Leisure in the Aftermath of Trauma: An Auto-Ethnography of Loss, Fear, and Working with Refugee Families
Bibliographic record
Abstract
In this auto-ethnographic study, I examine the tensions in conducting leisure research with communities experiencing trauma as a survivor of trauma. I use diary entries taken during times of coping with my mother’s illnesses, and during a two-week period working with Syrian refugee families in Lebanon, to explore narratives of leisure as a means to heal and belong. Results from a narrative analysis reflect (1) renegotiating an empathetic researcher identity after trauma; and (2) affordance for safe leisure and opportunities to heal. These narratives contribute to enriching understandings of self-compassion within leisure research, with a focus on the capacity for leisure researchers to meaningfully engage with their experiences to deepen their engagement with families. It is my hope findings from this study can aid researchers in reflecting on their own trauma and healing journeys – and in doing so strengthen their capacities to care for others and themselves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".