Infusing Indigenous Content and Treaty Education into Physical Education Teacher Education (PETE): A Collaborative Self-Study of Teacher Education Practices (S-STEP)
Bibliographic record
Abstract
This research responds to calls for the decolonization and indigenization of education and higher education spaces and institutions within Canada, specifically within the physical education (PE) and physical education teacher education (PETE) sub-disciplines. Recognizing our own responsibility to attend to decolonization and indigenization, we recently engaged in a collaborative self-study of our own teaching practice so that we might be better able to appropriately infuse Indigenous content and Treaty Education into our university’s PETE program. We identified this goal – infusing Indigenous content and Treaty Education – as one that would support us on our reconciliatory journeys as we aim to decolonize and indigenize our PETE spaces in authentic, genuine, and meaningful ways. Our collaborative self-study yielded findings related to our PETE practice, framed herein as six themes. Four of these themes relate to the tensions observed and felt (pushing past performative fears, inviting imposter syndrome, honouring the local Mi’kmaq contexts/peoples, critical friend[ships] as key) and two of the themes relate to our perceived positive outcomes and additional potential happenings (knowing better/doing better, possibilities beyond ‘simply’ infusing Indigenous content and Treaty Education). A discussion of these findings is offered and would be especially insightful to others who are interested in or engaged with self-study, indigenization and decolonization, and/or PE and/or PETE.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".