Getting a Quality Education: Indigenising Post-Secondary Institutions in Northern Ontario Through the Indigenous Quality Assurance Project
Bibliographic record
Abstract
In Canada, many institutions have signed onto Indigenous education manifestos such as College and Institutes Canada’s Indigenous Education Protocol and Universities Canada’s Principles on Indigenous Education which generally advocate for respect for Indigenous knowledge systems and the meaningful participation and representation of Indigenous peoples in the academy. These declarations of commitment to indigenization, while widely announced and promoted in the public domain, do little to communicate and provide evidence of effective implementation, as defined by Indigenous peoples. As a result, a growing number of Indigenous scholars have questioned the intent, depth, and outcomes of indigenization efforts and have implicated indigenization in the ongoing system of settler colonialism.In response, northern colleges in Ontario undertook the Indigenous quality assurance (IQA) project to develop Indigenous quality assurance standards and an implementation process complimentary to the colleges’ current audit-based quality assurance system. This article will discuss the development of the northern colleges’ IQA system and explore how the Indigenous quality assurance system can provide a tangible path forward to enact indigenization. In particular, the capacity of Indigenous quality assurance to address the calls by Indigenous scholars to ensure indigenization efforts are systemic, led by Indigenous peoples, everyone’s responsibility, and accountable to Indigenous peoples are explored.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.008 |
| 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".