Towards Decolonial Choreographies of Co-Resistance
Bibliographic record
Abstract
This article engages movement as a methodology for understanding the creative coalition work that we carried out for a project series called Into the Light (ITL) that used research from university archives to mount a museum exhibition and then develop an interactive public education site that counters histories and ongoing realities of colonial eugenics and their exclusionary ideas of what it means to be human in Canada’s educational institutions. We address different movement practices, both those initiated by ableist-colonial forces to destroy difference and by our coalition of co-resistors to affirm difference. We apply a decolonizing and Anishinaabe philosophical lens alongside a feminist disability-informed neomaterialist and dance studies one to theorize examples of ITL’s “choreographies of co-resistance”. Anishinaabe knowledge practices refuse and thus interfere with colonial-eugenic practices of erasure while enacting an ethic of self-determination and mutual respect for difference. The ripple effect of this decolonizing and difference-affirming interference reverberates through our words and moves at varying tempos through our bodies—traveling through flesh, holding up at bones, and passing through watery, stretchy connective tissue pathways. These are our choreographies of co-resistance as actions of mattering and world-building.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.022 | 0.091 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".