Structural Genocide and Institutionalized Racism in Canada: The Department of Indian Affairs and Framing of Indigenous Peoples
Bibliographic record
Abstract
This review problematizes the health and socio-economic disparity between Indigenous and non-Indigenous communities, which I argue is due to the role of the Canadian government. Specifically, I analyse the continuous process of Indigenous administrative subjugation under Canadian rule to uncover the intrinsic racial predilections of Canadian government policy toward First Nations peoples in Canada’s Prairie West provinces through the application of diagnostic frame analysis as a multidisciplinary research method to analyse how people understand situations and activities. My research results reveal the racialized marginalization of First Nation peoples through the administrative regimes in Canada as a continuous contemporary process established in the late nineteenth and twentieth century. In exposing the structural discrimination of First Nations peoples, my research introduces the reader to the concept of political master narratives, or ‘imaginaries’. These imaginaries foster the health and socio-economic disparities between Indigenous and non-Indigenous groups in Canadian society. The critical analysis of these historically structural government instituted imaginaries and the indirect, exponentially higher chances of tuberculosis and related diseases and deaths among Indigenous peoples’ challenge conclusions of the Truth and Reconciliation Commission (TRC) on cultural genocide. This study proposes structural genocide as a more accurate and inclusive term for the continuous institutional marginalization of not only Indigenous peoples as seen in this case study of the Department of Indian Affairs (DIA) but for all Indigenous peoples in Canada.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.036 | 0.032 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".