Structural racism and Indigenous Health: A critical reflection of Canada and Finland
Bibliographic record
Abstract
The purpose of the study was to broaden understanding of structural racism by examining the relationships between Indigenous peoples and nation-states in the context of education and how this affects Indigenous lives. This thesis delves into understanding both the theoretical and methodological contributions that more critical analyses can have on: the role of de-colonial approaches to Indigenous health research methodologies so that the most urgent health inequities are addressed through more rigorous and Indigenous specific research processes; and to improve our understanding of the complex interactions that historical and contemporary legacies of residential schools and boarding schools have on the health and well-being of Indigenous populations in Canada and Finland. The research design was a qualitative multiple case study informed by a public health critical race praxis. The study was completed in two phases; consisting of a literature study using content analysis of Indigenous research ethics protocols and policies, in Canada and the Nordic countries; and, three case studies developed from open ended questions from structured interview research comparing discriminatory experiences and its impact on self-perceived health with participants from Six Nations of the Grand River, Canada (n = 25) and the Sámi in Inari, Finland (n = 20); and their family members. The case studies were analyzed using both Western and Indigenous methodologies. Results of Phase one shows how Indigenous resistance to colonial structures within academia in Canada and Finland has resulted in dialogical processes to create an ethical space for working between the differing worldviews of academia and Indigenous communities with the aim to produce ethically valid knowledge. Phase two results shows that regardless of contextual differences of the experiences in Canada and Finland, the main parallel outcomes are similar, i.e. the teachings of shame received in these educational environments. This produces both vulnerabilities and resiliencies and the negative effects of shame require an ongoing healing journey for both individuals and their families and communities at large. Conclusion: For a more in depth understanding of structural racism and its influence on Indigenous health, investigations require methodological choices by both Western and Indigenous methodologies.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.114 | 0.038 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.010 | 0.017 |
| 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".