Challenging Colonial Norms and Attending to Presencing in Stories of Missing and Murdered Indigenous Women
Bibliographic record
Abstract
Scholarship related to missing and murdered Indigenous women in Canada has highlighted community resistance in response to traumatic losses. While sympathetic to other scholarship, I offer a different approach. I take on the challenging task of documenting resistance by missing and murdered women during and beyond their physical lifetimes. I attempt to engage in the delicate work of tracking what Leanne Simpson called “presencing” in her 2011 publication, Dancing on Our Turtle's Back: Stories of Nishnaabeg Re-creation, Resurgence and a New Emergence, in two fatalities. This is accomplished through a close examination of the death in 2013 of Kinew James, an Indigenous woman held in federal custody, and the homicide of twenty-year-old Alberta resident, Amber Tuccaro, in August 2010. In her qualitative studies on Indigenous girlhood, Sandrina de Finney encourages scholars to take up presencing to illuminate ways in which Indigenous girls' are actively engaged in anti-oppressive strategies. With her scholarship in mind, I examine news articles that report on Amber Tuccaro's and Kinew James' last words and actions to pinpoint their resistant strategies. Finally, I draw from the work of socio-legal scholars to explore presencing as a pathway to more transformative legal inquiry.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.060 | 0.066 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".