Secondary Education in Ontario: Effective Strategies for Prejudice Reduction Between Indigenous and Non-Indigenous Communities
Bibliographic record
Abstract
This policy brief uses the lens of prejudice reduction to scrutinize Ontario’s secondary education curriculum in efforts to promote reconciliation between Indigenous and non-Indigenous communities in Canada. Ever since the founding of Canada, Indigenous peoples have experienced prejudice and discrimination in Canadian society. This paper argues that quality education promoting prejudice reduction is the most viable method to mitigate contemporary conflict and disputes between two communities such as the Coastal GasLink pipeline dispute and the Wet’suwet’en protest, residential school trauma, and overrepresentation of Indigenous peoples in the criminal justice system. Furthermore, it critically analyzes the current education system of Ontario to reveal the lack of availability of Native Studies, the dismissal of Indigenous values, and the recommendation of outdated textbooks that present the experiences of Indigenous peoples in a non-holistic narrative. The paper then insists on the implementation of school-based prejudice reduction strategies in the Ontario Curriculum that have been found effective in promoting peace in the Middle East through CISEPO and education programs for the Israeli-Palestinian conflict. Lastly, the paper recommends various teaching methods and updated resources to encourage holistic learning and understanding of Indigenous experiences in order to reduce prejudice and promote reconciliation between Indigenous and non- Indigenous communities.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".