Telling My Tale: Reflections on the Process of Visual Storytelling for Children and Youth Living With Cystic Fibrosis and Muscular Dystrophy in Canada
Bibliographic record
Abstract
Storytelling is perennial to the human condition. We all tell stories and we all bear witness to the stories of others. According to narrative scholars, only certain stories are valorized in contemporary culture, while others go unrecognized. The inability to recognize ourselves and identities in contemporary cultural narratives can contribute to the silencing and muting of certain lives and voices. Young people with life-shortening conditions, such as cystic fibrosis (CF) and muscular dystrophy (MD), are rarely afforded the opportunity to have their stories heard and affirmed in contemporary cultural spaces. In this article, we reflect on the methodological process of engaging in a study known as “Telling My Tale,” that is, a storybook study featuring narratives and artwork by young people with CF and MD. Funded by the Social Sciences and Humanities Research Council of Canada, we critically reflect upon the methodological lessons, advances, and innovations we have learned, including theoretical musings, the process of exhibiting some of the artwork in a public art gallery, challenges faced along the way, analytical conundrums, and the role of technology in artistic creation for participants with limited hand function. In so doing, we hope to further methodological and theoretical development and innovation in narrative and artistic traditions to better center the voices, lives, tales, and experiences of young people with life-shortening conditions.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.045 | 0.037 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| 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".