Racial discourse as praxis: Reflections on becoming an anti-racist through a university-level course power and education
Bibliographic record
Abstract
This qualitative study captures the narrative of six culturally diverse doctoral students’ after they have participated in an anti-racist course. The course immersed students in literature surrounding race and school-based criminalization to heighten their awareness of ways internalized racism and perpetuated anti-Black racism harms the students they educate. Within this study, we conducted two focus group sessions and analyzed responses from six doctoral students at Wayne State University. As they reflected on the racism being perpetuated in education and their daily challenges within a system based on white supremacy, they ignited their determination to work for racial equity having realized their past contributions to perpetuating these ideologies. Critical race theory (CRT) was used to take a deeper look at the historical and ongoing aspect of race and racism in American culture, including within the education system. The participants of color discussed their personal experiences with oppression as well as their initial resistance to become anti-racist. At the end of the course, that resistance evolved into an increased desire for racial advocacy. This study implicates that teacher education and educational leadership course work should promote comprehension of racism, provide opportunities for scholars to grow moral courage, and the need to advocate for curriculum reform at all levels.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.038 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.012 |
| 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".