Canada's Missing and Murdered Indigenous People and the Imperative for a More Inclusive Perspective
Bibliographic record
Abstract
The National Inquiry into Missing and Murdered Indigenous Women and Girls (MMIWG) emerged to bring attention to the overrepresentation of missing and murdered Indigenous women in Canada. It has raised awareness about systemic racism and sexism as well as social and economic conditions experienced by Canada's Indigenous population. Yet, research shows that Indigenous males are the most likely to be murdered in Canada (Mulligan, Axford, & Soecki, 2016). Since Indigenous men are going missing and are murdered in disturbing numbers, and they are fathers, brothers, and sons to Indigenous women and girls, it is understandable that many in the Indigenous community wanted to include them in the inquiry. Our analysis explores how the MMIWG and discourses about inclusion and exclusion have been framed in ways that limit interpretations about the root causes of problems experienced by Indigenous people, especially when they exclude an important part of the Indigenous population—Indigenous males. We draw upon Indigenous perceptions of the inquiry and analyses of social norms and stereotypes in order to explore the conflicting positions and experiences associated with missing and murdered Indigenous people in Canada. We conclude by exploring the need for a more comprehensive inquiry. We recognize that a holistic model of inquiry that honours the voices of Indigenous communities is crucial to a proper investigation into missing and murdered Indigenous people in Canada.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.065 | 0.037 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".