Structural violence in Canada : the role of Winnipeg educators in decolonization and reconciliation between Indigenous and non-Indigenous peoples
Bibliographic record
Abstract
As a result of the colonial history of Canada, and years of imposed structural violence, direct violence and assimilation policies, there is a need for awareness in of how Indigenous peoples have suffered. The history curriculum in schools can be improved, and decolonization and reconciliation can be considered as goals. \n \nThis thesis explores the meanings of structural violence, decolonization and reconciliation in the context of Canada, asking what local educators in Winnipeg are doing to promote awareness of these issues in their fields. Using content analysis to analyse and code multiple data sources, this study attempts to uncover what is missing from current education systems in Winnipeg, and what can be done to change this. \n \nAfter introducing theories education in peacebuilding, decolonization, and the Truth and Reconciliation Commission, the main theories of the study are introduced: structural violence and cultural violence. From five in-depth interviews with educators, five main points of improvement were extracted from the interview data. This was then compared to the Grade 11 "Canadian History" curriculum, and subsequently related to the Truth and Reconciliation Commission's fourth chapter from the report, "Education for Reconciliation". \n \nMy study finds that some of the main points from the interviews are already present in the provincial curriculum, but all have some space for improvement. Structural violence is indeed a pressing issue in improving the quality of life of Indigenous peoples, and education and awareness of these issues can help to deconstruct the structural violence.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".