Charting a Way Forward: Intersections of race and space in establishing identity as an African-Canadian teacher educator
Bibliographic record
Abstract
In Canada, most universities and their classrooms are often constructed as rational and neutral spaces where interaction between professors and students is free of the influences of race, class, and gender. Implicit in such a construction is the assumption that the university is a purely White space where students from the dominant racial group expect the professorate to be of similar race. The presence of a non-White teacher educator in such an environment disrupts such fictional constructions, generating tensions and resistance that complicate teaching and learning. This self-study describes my experience as an African-Canadian teacher educator and the intersections of race and space in a faculty of education landscape enmeshed in an ongoing struggle of decolonization. Along with self-study methodology, I use critical race theory and feminist post-structural theory to analyze the construction of my racial identity and relations of power in a White settler society. I explore how the intersection of race and space in my identity formation can be used to understand how my students are produced historically and culturally. The self-study provides insights into the production of teacher, student, and institution identities in a racialized place. It reveals how individuals and institutions are implicated in the work that needs to be done to enable diverse groups to interact meaningfully in critical, caring, and transformative ways.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.070 | 0.042 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".