"What makes a great story?: Multidisciplinary and International Perspectives On Digital Stories By Youth Formerly In Foster Care In Canada
Bibliographic record
Abstract
What makes a great story? This qualitative arts-based dissertation study explores multidisciplinary and international perspectives on digital stories created by youth formerly in foster care. Over Skype, thirty-five participants from the arts, healthcare, education, and social services sectors watched three short digital stories about experiences of youth in foster care. Then, each participated in a 90 minute semi-structured interview to discuss the value, impact, and potential for digital storytelling to influence social change.\n\nAll participants spoke about how the three digital stories presented honest and personal experiences that contrast dramatically with stories presented in the media about foster care. After viewing these stories, all participants asserted that there is a need for the creation and sharing of authentic and emotional stories that connect with specific audiences to subvert idealistic narratives in the media about youth currently and formerly in foster care.\n\nI drew on participant narratives using Constructivist Grounded Theory approaches to develop the 4A model to describe the attributes of great stories: Anticipation, Actualization, Affect, and Authenticity. I also created seven multimodal outputs that contributed to the shaping of the findings and enhanced reflexive praxis.\n\nThe implications of this work varies across disciplines. Digital storytelling facilitators may develop insights into better supporting future participants to think critically about the impact and value of their stories before they write them. Artists may consider how best to employ their aesthetic skills and techniques to create compelling and storied artworks. Social service professionals may consider how to further leverage stories to build empathy and positively impact care delivery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".