Navigating Racialized Spaces in Academia: Critical Reflections from a Roundtable
Bibliographic record
Abstract
Canadian Association of University Teachers (2010). The Canadian Association of University Teachers. (Ryan, Pollock, & Antonelli, 2007) and an ongoing challenge among scholars of color. Such challenges include: faculty and staff support, curriculum development, and feelings of validity. Reflecting back on discussions of race, it is important to note that these challenges are shared and valid. Despite an increase in the diversity of the post-secondary student population in Canada, professors identifying themselves as ethnic and cultural diversity are only 17%, according to the Canadian Association of Teachers (Ryan, Pollock & Antonelli, 2007), and this represents a perpetual challenge for racialized teachers. The lack of diversity sends a strong message to all students: creators of knowledge are only a minority elite. These challenges include difficulties in supporting faculty and staff, developing their curriculum and feeling of validity in the face of predominantly white institutions. Reflecting on discussions about race,
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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 | 0.049 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.088 | 0.040 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.019 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".