Tricksterdom in narratives of young adult cancer: Performances of uncertainty, subversion, and possibility.
Bibliographic record
Abstract
OBJECTIVE: As people with cancer attempt the difficult task of giving voice to life with illness, they often turn to mythic figures and stories (e.g., when people talk about battling cancer or embarking on a journey toward recovery). Little attention has been paid to the mythic figure of the trickster, recently identified by Arthur Frank (2009) as a prominent trope in some narrative accounts of illness. We investigated the prevalence of 3 tricksterly themes expressed within young adults' stories of cancer: destabilizing social or cosmic order (uncertainty), challenging dominant expectations for human life (subversion), and exploring alternative ways of viewing the world (possibility). METHOD: We recruited 21 young adults with cancer from across Canada and conducted semistructured interviews. We then analyzed their stories using some elements of thematic, structural, and dialogical/performative narrative analysis-drawing attention to what was told and how/to whom were they told (Crossley, 2000; Frank, 2012; Riessman, 2008). RESULTS: We describe each of the 3 themes in turn (i.e., uncertainty, subversion, and possibility) using excerpts from 6 interview transcripts, and show how they meaningfully converge into an interpretive framework of tricksterdom. CONCLUSION: We conclude that the 3 themes of uncertainty, subversion, and possibility seem to come together as tricksterly performances, disrupting audiences' expectations of more typical forms of cancer narratives and calling attention to less familiar, structured, and "tellable" ways of narrating illness.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".