"I strive to be the teacher I needed when I was in school:" Exploring Anti-Racist Teaching in Edmonton, Alberta
Bibliographic record
Abstract
Edmonton is a growing Canadian capital city, currently known for its affordability and stability. With more people immigrating to Edmonton from both within Canada and other countries, the population grows increasingly diverse. With a diverse population comes the need for teaching and learning that addresses the needs of diverse students, something that, historically, teachers have been ill-equipped to do. While anti-racist teaching pedagogy has increased in popularity and overall visibility in the past few years, it is still reasonably underdeveloped in Edmonton. This study explores teacher thoughts and opinions about issues of race, racism, identity, and whether anti-racism is being adequately addressed in Edmonton school districts. By surveying 28 teachers in the Greater Edmonton Area, this study analyses what Edmonton teachers have been doing, or not doing, to practice anti-racism in their classrooms. Overall, participants demonstrated their respective understandings of anti-racism, as well as a developed knowledge of the issues that need to be addressed within their respective districts. Further, myself and the participants conclude that anti-racism is not only a pedagogical implement that contributes to their classrooms meaningfully, but one that should contribute to all classrooms.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.012 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".