Collective Digital Storytelling in Community-based co-design projects. An Emergent Approach
Bibliographic record
Abstract
Digital storytelling (DST) can play a critical role in co-design initiatives involving local communities, as a method for bridging exploratory phases and co-design processes. The paper draws on three case studies of collective DST in underserved locations. While DST enabled groups to present themselves and their communities, its evolution showed that activities crystallized into creative concepts and community-driven projects that generated new ideas, new collaboration pathways and new networking capabilities. The structured analysis of these case studies can be used by researchers looking to spur grassroots initiative and encourage local participation and engagement in community-based design.La narration numérique peut jouer un rôle essentiel dans les initiatives de co-design avec des communautés locales, en tant que méthode pour passer de la phase exploratoire de la recherche au processus de co-design. L’article se fonde sur trois études de cas de narration numériques collectives dans des communautés défavorisées. La narration numérique a donnée aux groups la possibilité de se présenter tandis que son processus génératif a cristallisé dans des concepts créatifs et des projets communautaires porteurs de nouvelles idées, voies de collaboration et capacités de réseautage. L'analyse structurée de ces études peut être utilisée par les chercheurs intéressés à stimuler l'initiative locale et à encourager la participation et l'engagement communautaires.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".