Traditional storytelling: an effective Indigenous research methodology and its implications for environmental research
Bibliographic record
Abstract
Using traditional Western research methods to explore Indigenous perspectives has often been felt by the Indigenous people themselves to be inappropriate and ineffective in gathering information and promoting discussion. On the contrary, using traditional storytelling as a research method links Indigenous worldviews, shaping the approach of the research; the theoretical and conceptual frameworks; and the epistemology, methodology, and ethics. The aims of this article are to (a) explore the essential elements and the value of traditional storytelling for culturally appropriate Indigenous research; (b) develop a model of a collaborative community and university research alliance, looking at how to address community concerns and gather data that will inform decision-making and help the community prepare for the future; (c) build up and strengthen research capacity among Indigenous communities in collaboration with Indigenous Elders and Knowledge-holders; and (d) discuss how to more fully engage Indigenous people in the research process. In two case studies with Indigenous and immigrant communities in Canada and Bangladesh that are grounded in the relational ways of participatory action research, the author found that traditional storytelling as a research method could lead to culturally appropriate research, build trust between participants and researcher, build a bridge between Western and Indigenous research, and deconstruct meanings of research. The article ends with a discussion of the implications of using traditional storytelling in empowering both research participants and researcher.
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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 | 0.155 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".