Digital storytelling to promote disability-inclusive research in Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it