On Experiencing Discomfort: Two Racialized Canadian Teacher Educators Reflect on their Personal and Professional
Bibliographic record
Abstract
In this article, we share our journeys as 2 different racialized teacher educators, who took a group of teacher candidates from a Canadian teacher education program to Tanzania East Africa. The 18 teacher candidates we took with us were enrolled in a Bachelor of Education program and were going to Tanzania to do their international service learning practicum. To analyze our experiences and observations in Tanzania we used critical race theory as a tool. Critical race theorists like Ladson-Billings & Tate (1995) and Dixson & Rousseau (2006) argue that race is a significant factor in determining experiences of social inequity people encounter. Thus throughout this article, we provide an analysis of race that helps open up insights into our thinking as racialized teacher educators. As a result of the analysis, we suggest that critical race theory needs to be explored and discussed in our international service learning seminar course to better prepare teacher candidates who are interested in participating in international service learning practicums.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".