Policy Matters! Wholistically Supporting Indigenous Students’ Journey to and Through Canadian Post-secondary Education
Bibliographic record
Abstract
Over the last 30 years, Canadian post-secondary institutions have been developing specific programs, supports, and services to support Indigenous student access to and persistence through post-secondary education. Part of the ongoing work of decolonization, reconciliation, and Indigenization is challenging a colonially imposed definition of success (e.g., GPA, degree completion within 4 years) to consider Indigenous students’ experiences and their success more wholistically. This project aimed to identify how Indigenous student success is supported by institutional policies, programs, and practices. The research process included conducting an Indigenous qualitative content analysis of 74 universities and 158 colleges (i.e., public, English, and French) websites along with six semi-structured interviews with various program providers. This article examines how Canadian post-secondary institutions can wholistically support Indigenous students’ educational journeys through effective policies, programs, and practices that enhance access, facilitate transitions, and foster persistence. The analysis found 47 access, 64 transition, and 50 persistence programs specifically for Indigenous students. The analysis also raised crucial questions related to program sustainability. Further research is needed to understand the impact of these initiatives on the persistence of the next seven generations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".