Settler-Colonial Violence and Commodification of Indigenous Bodies: Canada’s Missing and Murdered Indigenous Women and Girls in Marie Clements’ Play <i>The Unnatural and Accidental Women</i> and Carl Bessai’s Film <i>Unnatural & Accidental</i>
Bibliographic record
Abstract
The Missing and Murdered Indigenous Women and Girls (MMIWG) phenomenon has taken monstrous proportions in Canada, having become a national concern, with many organizations, institutions, communities, and individual groups dealing with missing loved ones in their midst. The state of Canada, academic studies, books, documentaries, short and feature films, art as well as theatre plays have taken on this phenomenon – all with the purpose of raising awareness in both Indigenous and non-Indigenous groups to wake up the nation to systemic rape and killing of Indigenous women and girls, and also to warn and train such women and girls to consciously work on not becoming the next victim. This article looks at the sexed and raced colonization and commodification of Indigenous women’s bodies and the genealogy of white male views on and stereotypes of Indigenous women in Canadian history and North American film history. Tied in with these discussions, the article analyzes and compares Marie Clements’s play The Unnatural and Accidental Women (2000) and Carl Bessai’s film Unnatural & Accidental (2006) and looks at how both play and feature film expose the confluence of racist stereotypes and settler-colonial male violence and critically contextualize one mass murder case as emblematic of the MMIWG phenomenon.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.044 | 0.027 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".