Digital storytelling for community resilience: Art as public pedagogy
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.Positioning digital storytelling as public pedagogy, this article examines how this multimodal artistic practice fosters community resilience through the intersections of art and social movements. Through examination of two case studies—mini-documentary videos highlighting senior immigrants’ resilience during COVID-19 and a series of soundscapes developed for an urban ecological soundwalk—we explore how these collaborative, relational storytelling initiatives amplify marginalized voices and mobilize collective action. The research identifies three interconnected themes: digital storytelling as public pedagogy, narrative co-creation as catalyst for social movements, and digital platforms as tools for intergenerational and ecological advocacy. These cases illustrate how arts-based digital practices create spaces for dialogue while preserving cultural and ecological knowledge. By bridging individual and collective experiences, digital storytelling emerges as a vital medium for constructing resilience imaginaries and building collective adaptive capacity through creative expression and participatory engagement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.003 |
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".