"This is a continuation of genocide" : Examining the pathologization of Indigeneity in the 2016 suicide crisis and state of emergency in Attawapiskat First Nation
Bibliographic record
Abstract
This research paper concerns the co-constitution of pathology and Indigeneity in the settler-colonial state of Canada by 1) conducting a media analysis of national media coverage from one major Canadian newspaper, National Post, on the suicide crisis and state of emergency in Attawapiskat First Nations in 2016, and 2) engaging in a critical theoretical discussion of the neoliberal political economy and resultant social conditions in which Attawapiskat First Nations is enmeshed, in order to contextualize conditions of ill-health and account for the emergence and function of pathological frames of reference. Through engagement with critical Indigenous and race scholarship and the methodological practice of refusal of damage-centred research (Simpson, 2007; Tuck, 2009; Tuck and Yang, 2014), I examine the ways in which Indigenous peoples' bodies and minds are situated as the problems to be 'fixed' and how historical and material social conditions are narrated as 'symptoms' of Indigenous racialized subjectivity (Million, 2013; Tam, 2013) such that Indigeneity is made synonymous with pathology. Specifically, I address how inclusion of Indigenous subjects into the discursive arena of 'disability'- through equation of pathological frames of reference with Indigenous subjectivities as signified by the application of diagnoses of 'trauma', 'mental illness' and the stigmatization/psychiatrization of Indigenous felt experiences- does not translate into a mitigation of settler-colonial violence against Indigenous subjects. Instead, I demonstrate how this equation functions ideologically to negate the self-determination of Indigenous subjects, legitimize neoliberal, settler-colonial strategies of land theft and dispossession, and ultimately conceal from view the settler-colonial relations through which Indigenous subjects are harmed, injured, disabled and killed. Furthermore, I show how this necropolitical function of colonial violence is always/already legitimized as necessary for the (racial) project of modernity and concealed through trauma discourse and narratives of pathology that equate progress and development with 'healing.'
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".