“Just” Stories or “Just Stories”?: Mixed Media Storytelling as a Prism for Environmental Justice and Decolonial Futures
Bibliographic record
Abstract
Our lives and the lives of those we study are full of stories. Stories are never mere stories. Qualitative researchers who document, hear, and listen to participant lived-experiences encounter and witness the intimate spaces of people’s everyday lives. Researchers thus find themselves in the position of translator between diverse communities: those affected by policies, the academy and public officials. For academic-activists committed to listening to situated stories in order to improve public policy, several critical questions emerge: How do we do justice to these stories? What are the ethics of engagement involved in telling stories about those who share their knowledges and lived-experiences with us? Can storytelling bridge positivist and post-positivist research methods? Do policymakers listen to stories? How? What can researchers learn from Indigenous storytelling methods to envision decolonial, sustainable futures? To respond to these critical questions, this paper draws from literature in community-engaged research, critical policy studies, interpretive research methods, Indigenous research methods, political ethnography, visual methods and social justice research to argue that stories arenever simply or just stories, but in fact have the potential to be radical tools of change for social and environmental justice. As will be discussed with reference to three mixed media storytelling projects that involved the co-creation of digital stories with Indigenous communities in Canada, stories can intervene on dominant narratives, create space for counternarratives and in doing so challenge the settler-colonial status quo in pursuit of decolonial 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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.044 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| 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".