Sharing Indigenous Knowledge through intergenerational digital storytelling: Design of a workshop engaging Elders and youth
Bibliographic record
Abstract
Within many First Nations communities, storytelling, led by Elders recognized as knowledge holders, is a deeply valued aspect of teaching and learning history, language, place, culture, and Indigenous Knowledge. The purpose of this study was to design and evaluate the first iteration of an intergenerational digital storytelling workshop that brought Elders and school children from a First Nations community in Canada together to co-create digital stories and share local Indigenous Knowledge. Using a community-based participatory research approach, the research team, school, and community members designed and implemented a ten-session program held during a six-week period. Thirty-one grade six and seven students were paired with thirteen Elders. Elders told a range of stories including personal experiences, legends, and local knowledge of hunting and medicine. Students created a digital version of the stories, adding images, sound, and music. Students learned about local knowledge, built connections with Elders, and increased their digital literacy. Future recommendations included expanding the program over the full year and integrating it with other classes such as having Carrier language as a key component of the digital stories and incorporating art and music created by the students.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".