Structural Oppressions Facing Indigenous Students in Canadian Education
Bibliographic record
Abstract
Indigenous students in Canada do not graduate from secondary school at the same rate as their non-Indigenous peers. We argue in this article that the lower graduation rate is due to the many structural oppressions that Indigenous people experience. The authors concentrate on four large-scale oppressions that commonly face Indigenous students: poverty, suppression oftheir identities, racism and gender violence. Indigenous worldviews, perspectives and realities are at variance with the country’s dominant educational, economic and political institutions and negatively impact the self-esteem of Indigenous students. As well, their sociocultural identities are distinct, and undermined by the stereotypes and specific attributes designated to them. In order to counter these negative conditions and cultivate minopimaatisiiwin which is the Anishinaawpe concept of “living well” and “well-being”, we recognize a self-determination framework as essential to Indigenous education. A self-determination framework serves to strengthen Indigenous students’ identities to protect against dominant oppressions, foster resilience, and motivate younger generations towards improved educational outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".