Virtual Digital Storytelling: Building Solidarity in Transnational Participatory Research
Bibliographic record
Abstract
Digital storytelling creates short videos that tell a story by combining images and/or video footage with an audio narrative, usually set to background music. In participatory research, they enable participants to tell their own stories and generate a rich form of multimodal data that can be shared both personally and through knowledge mobilization. In this article, we describe our use of virtual digital storytelling, which brought together 12 youth participants ages 18–25 from 11 different countries in Africa and Asia to create and share digital stories about their activism for gender transformative education. We used Microsoft Teams to hold two focus group discussions with three groups of 3–5 participants and connect individually with participants to create their digital stories. The project was designed and implemented in partnership with Transform Education, a global youth-led feminist activism coalition. We describe significant opportunities related to fostering transnational connections and providing participants with ownership and control of the stories. We also highlight logistical and ethical challenges surrounding internet connectivity, trauma disclosures, and use of images in research and provide recommendations for navigating them. Ultimately, we advocate for virtual digital storytelling as a viable means of engaging geographically disparate participants in meaningful participatory art-based research.
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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 | 0.090 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".