TRANSFORMING GRADUATE STUDIES THROUGH DECOLONIZATION: SHARING THE LEARNING JOURNEY OF A SPECIALIZED COHORT
Bibliographic record
Abstract
This study traces the learning journey of primarily non-Indigenous educators who challenged the legacy of colonization in schools, and worked to decolonize their practice, through their participation in a specialized graduate cohort. Drawing upon sharing circle conversations, we highlight themes that emerged from our research. Educators reported their sense of agency to transform and change their practice increased with the support of a critical and caring learning community. In nourishing their learning spirits, educators were able to begin to decolonize education, an ongoing challenge that requires valuing Indigenous people, languages, and land, and building inter-cultural understanding. This study is an example of how graduate programming can begin to address the Calls to Action of the Truth and Reconciliation Commission.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".