Building the Settler Colonial Order: Police (In)Actions in Response to Violence Against Indigenous Women in “Canada”
Bibliographic record
Abstract
In this article we focus on missing and murdered Indigenous women, girls, and 2SLGBTQQIA people in “Canada.” We theorize narratives that police employ to respond to this violence. Using a broad data sample of testimonies across “Canada,” this article contributes to understanding how police (in)actions make sense of, justify, and dismiss violence against Indigenous women, girls, and 2SLGBTQQIA people. We draw from 48 in-depth semi-structured interviews with Indigenous peoples in Toronto and other “Canadian” cities and 219 testimonies from the Canadian National Inquiry into Missing and Murdered Indigenous Women and Girls (NIMMIWG). The analysis finds that police repeatedly use similar frames (topics), styles (linguistic and behavioral strategies), and storylines (narratives) to respond to violence against Indigenous peoples. While framing Indigenous women, girls, and 2SLGBTQQIA people as deviants helps police make sense of and explain violence against them, the use of verbal and behavioral strategies (indifference, callousness, and lack of information) and storylines (“they were [insert pathologizing frame]” and “there’s nothing we can do”) allows police to dismiss and justify acts of violence. We argue that the frames, styles, and storylines employed by police perpetuate violence. Police (in)actions are fundamental to achieving settler colonialism’s ideological and material dimensions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.024 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".