Story‐Making as Methodology: Disrupting Dominant Stories through Multimedia Storytelling
Bibliographic record
Abstract
In this essay, we discuss multimedia story-making methodologies developed through Re•Vision: The Centre for Art and Social Justice that investigates the power of the arts, especially story, to positively influence decision makers in diverse sectors. Our story-making methodology brings together majority and minoritized creators to represent previously unattended experiences (e.g., around mind-body differences, queer sexuality, urban Indigenous identity, and Inuit cultural voice) with an aim to building understanding and shifting policies/practices that create barriers to social inclusion and justice. We analyze our ongoing efforts to rework our storytelling methodology, spotlighting acts of revising carried out by facilitators and researchers as they/we redefine methodological terms for each storytelling context, by researcher-storytellers as they/we rework material from our lives, and by receivers of the stories as we revise our assumptions about particular embodied histories and how they are defined within dominant cultural narratives and institutional structures. This methodology, we argue, contributes to the existing qualitative lexicon by providing innovative new approaches not only for chronicling marginalized/misrepresented experiences and critically researching selves, but also for scaffolding intersectional alliances and for imagining more just futures.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".