Fashioning decolonization: telling stories of Canadian Indigenous women through fashion hacking
Bibliographic record
Abstract
Decolonization is a complex, often discussed in academic or political environments, it is challenging to discover tangible ways for individuals to practice it in daily life. A gap between the personal understanding decolonization and the academic definitions of decolonization was identified through oral history interviews, so this research shares the unique stories of eight Canadian indigenous women through fashion as a way to inform a more accessible and embodied definition of decolonization. The participants took part in a fashion-hacking workshop to began to answer the questions: What does the lived experience of decolonization look and feel like for Indigenous women? How do Indigenous women think about decolonization in the personal aspects of their life and how can that be expressed through fashion? The format of this paper explores different perspectives and ways of knowing by jumping back and forth between storytelling, art, Indigenous and Western academic research.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".