Telling stories: Exploring research storytelling as a meaningful approach to knowledge mobilization with Indigenous research collaborators and diverse audiences in community‐based participatory research
Bibliographic record
Abstract
A growing number of geographers seek to communicate their research to audiences beyond the academy. Community‐based and participatory action research models have been developed, in part, with this goal in mind. Yet despite many promising developments in the way research is conducted and disseminated, researchers continue to seek methods to better reflect the “culture and context” of the communities with whom they work. During my doctoral research on homelessness in the Northwest Territories, I encountered a significant disconnect between the emotive, personal narratives of homelessness that I was collecting and more conventional approaches to research dissemination. In search of a method of dissemination to engage more meaningfully with research collaborators as well as the broader public, I turned to my creative writing work. In this article, I draw from “The komatik lesson” to discuss my first effort at research storytelling. I suggest that research storytelling is particularly well suited to community‐based participatory research, as we explore methods to present findings in ways that are more culturally appropriate to the communities in which the research takes place. This is especially so in collaborative research with Indigenous communities, where storytelling and knowledge sharing are often one and the same. However, I also discuss the ways in which combining my creative writing interests with my doctoral research has been an uneasy fit, forcing me to question how to tell a good story while giving due diligence to the role that academic research has played in its development. Drawing on the outcomes and challenges I encountered, I offer an understanding of what research storytelling is, and how it might be used to advance community‐based participatory research with Indigenous communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.057 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".