Achieving Equity in Graduation Rates and Other Indicators of Success for Indigenous Learners in Canada
Bibliographic record
Abstract
This research project was designed to attend to inequity for Indigenous students, communities, and knowledges in a northern British Columbian district. The aims of the article are to share the systemic and individual transformation for Indigenous learners and their families based on the strengths and barriers they perceive in the system. Presented here are the results of extensive engagement with students, parents or guardians, teachers, administrators, and Indigenous communities that have led to novel practical approaches to governance, policy, programmatic design, and practice in a mainstream school district, resulting in improved school experiences for Indigenous learners. Through this research we illuminate the voices of Indigenous students and show how they guided the pursuit of equity in a Canadian school district. We examined the unconscious colonial agenda to understand how it emerges visibly and invisibly in a given context (Louie, 2020), while simultaneously creating distinct responses emerging from the teachings of Indigenous stakeholders and rights holders. Internal and external pressures on school districts often result in urgent demands for transformation, or at minimum, the urgent shift in perception of transformation (Daigle, 2019), but real and sustaining change cannot be rushed, borrowed, or created in isolation from the rest of the system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".