Storytelling in a digital age: digital storytelling as an emerging narrative method for preserving and promoting indigenous oral wisdom
Bibliographic record
Abstract
This article outlines the methodological process of a transdisciplinary team of indigenous and nonindigenous individuals, who came together in early 2009 to develop a digital narrative method to engage a remote community in northern Labrador in a research project examining the linkages between climate change and physical, mental, emotional, and spiritual health and well-being. Desiring to find a method that was locally appropriate and resonant with the narrative wisdom of the community, yet cognizant of the limitations of interview-based narrative research, our team sought to discover an indigenous method that united the digital media with storytelling. Using a case study that illustrates the usage of digital storytelling within an indigenous community, this article will share how digital storytelling can stand as a community-driven methodological strategy that addresses, and moves beyond, the limitations of narrative research and the issues of colonization of research and the Western analytic project. In so doing, this emerging method can preserve and promote indigenous oral wisdom, while engaging community members, developing capacities, and celebrating myriad stories, lived experiences, and lifeworlds.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 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".