“I Think of my Classroom as a Place of Healing”: Experiences of Indigenous Students in a Community-Based Master of Education Program in Saskatchewan
Bibliographic record
Abstract
An increase in the number of Indigenous teachers and education administrators is an important way to help improve Indigenous educational outcomes. However, while Indigenous teacher education programs in western Canada are registering increasing enrolments, master of education programs that prioritize Indigenous perspectives and pedagogies are rare in Canada. Using conversational method, this study examines experiences of six Indigenous students in a community-based master of education program that is a first of its kind in western Canada. The program is delivered by an Indigenous institution in partnership with a public university. The study is grounded in an Indigenous paradigm, namely, the Nehinuw (Cree) concepts of teaching and learning. Content analysis of data revealed five themes and sub-themes: (a) self-doubt; (b) a feeling of guilt as a result of family-work-school conflict; (c) self-advocacy; (d) re/connection with self, culture, and heritage; and (e) professional transformation. In general, a master of education degree is a requirement for educational administration positions including vice principal, principal, and superintendent. Understanding and acting upon the kinds of strategies that could enhance the success of Indigenous students in graduate programs is a key policy step in addressing the existing gaps in educational attainment between Indigenous and non-Indigenous Canadians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| 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".