“Are We Truly Disruptors?” A Thematic Analysis of Two Canadian Teachers Disrupting the Status Quo in their Classrooms
Bibliographic record
Abstract
This critical qualitative study looked to better understand the education system as an oppressive force, as well as highlight the strategies current Canadian teachers use to resist racism and colonialism in their classrooms. Using an inductive thematic analysis framework, my data sample consisted of 9 episodes from the podcast series The Chey and Pav Show: Teachers Talking Teaching featuring Toronto-based educators Chey Cheney and Pav (Pawan) Wander. The analysis identified two overarching themes pertaining to Pedagogy and Subject Content. The findings indicated that the educators have undergone a personal journey of social justice learning consisting of reflectivity and accountability, which in turn allowed them to employ transformative teaching strategies and build authentic relationships with students. Further, the results of the study demonstrated how the educators challenged Eurocentrism in the subject matter they taught with the use of anti-racist, anti-colonial, and culturally responsive approaches. These findings address the existing gap in the literature pertaining to the methods used by Canadian public education teachers to create more socially just and anti-oppressive classrooms.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.041 | 0.028 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".