Re/turning the gaze: unsettling settler logics through multimedia storytelling
Bibliographic record
Abstract
Drawing on three decolonizing feminist arts-based research projects, we discuss possibilities for making multimedia stories that counter, respond to, and re/turn the heteropatriarchal settler colonial gaze. All three projects use a participatory videomaking method that involves misrepresented communities producing short films to advance social justice. We explore how the creation of narrative videos by Indigenous researchers, educators, students, and artists, and allies offer multiple avenues for re-turning the interconnected gaze of heteropatriarchy and colonialism, creating a feminist decolonizing aesthetic—an embedded and embodied aesthetic—that consciously weaves together process and form. We consider moments of re/turning, refusing, and reckoning with the colonizing masculinist gaze through analyzing the videos organized along three themes: the catalyzing effects of educational experiences, both destructive and empowering; the complex decolonizing affects of love; and the pervasive and debilitating effects of everyday gendered racism in/beyond school. We offer reflective analysis by drawing on feminist decolonializing theories on the power in looking and in interrogating heteropatriarchal colonial discourse. Each of the stories that we analyze re/turns the interconnected gaze of heteropatriarchy/colonialism in ways that extend responsibility for colonialism, for misogyny, for racism, for gender/sexual normativity, to the viewers of these films—to all of us.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".