Arts-Informed Storytelling: How Arts-Informed Research was Used with Six Indigenous Peoples in London, Ont.
Bibliographic record
Abstract
This doctoral project outlines the methodological process of using arts-informed storytelling to illustrate the experiences of six urban Indigenous Peoples who live in London, Ontario. The strategy is a qualitative method influenced by artistic processes and expressive qualities to deepen understanding of human experiences. The technique involves illustrating personal experiences, often using multimedia in photographs, artwork, text, audio, music, crafts, or some combination of these cultural products. I aim to build on previous studies that use storytelling methods in their research by employing this participatory and multimodal approach. This dissertation tries to make new contributions to media studies by offering a first-person account of how my experience using arts-informed storytelling unfolded as an emergent and iterative process.\nFor this study, I facilitated a storytelling process by asking six Indigenous contributors to share and tell their own stories related to life and living in the city. I also interviewed the contributors about their experiences in creating their works. In this dissertation, I explore what the stories suggest about the experiences of the six contributors. I also document how arts-informed storytelling can be an essential addition to qualitative approaches by addressing the complexities, potential opportunities, and limitations of using the method. While this dissertation offers a widely accepted way of documenting my doctoral project’s findings, I also curated a virtual exhibition for knowledge translation to make the creators’ stories more accessible beyond academic contexts.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".