Digital storytelling to promote disability-inclusive research in Africa
Bibliographic record
Abstract
Background: Digital stories have been shown to be effective in sharing information. The Partnerships for Inclusive Research and Learning (PIRL) was a 4-year international participatory research project focussed on the digital divide in inclusive research. Objectives: Members of PIRL share their experience of using digital storytelling to get key messages from the project to a wide range of people. Method: Members of PIRL were invited to develop digital stories and create project-specific guidelines for digital story development. Seven people participated in workshops given by experts, read literature, watched digital stories and discussed how to create digital stories. Results: The group created six digital stories, each one addressing a different aspect related to disability-inclusive research, with many having a focus on Africa and the creation of credible African evidence. The importance of assisting community members to think about and support research and evidence creation was one of the goals of the project. The videos provide an avenue to share insights about disability-inclusive development research. Group members stated that being part of the process significantly improved their understanding of translating evidence into formats that are more understandable. Conclusion: Creating digital stories requires commitment, a significant amount of time, access to digital tools, and financial resources. Working collaboratively on this project was not only meaningful but also encouraged positive working relationships and fostered critical thinking. Contribution: This article contributes to a better understanding of ways in which digital storytelling can be used in knowledge-sharing strategies to promote disability inclusion.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".