Critical community building in action: a triad of faculty, graduate and undergraduate students working for racial justice
Bibliographic record
Abstract
This article considers what critical community building might look like among colleagues at a university representing one faculty member, one doctoral candidate, and one undergraduate student. Using critical autoethnography-self-study, we analyze our journal reflections, presentations, teaching, and dialogues to better understand our approaches with teaching Critical Race Theory. This research asks: How do colleagues across power dynamics and positionalities learn from each other, and work collaboratively to teach about race and racism at a predominantly white institution? Our findings indicate that this sort of work requires relationships, shared vulnerability, and an understanding of our journeys to becoming critical pedagogues. We find value in this work due to its focus on collaboration across power dynamics (i.e. rank of professor, graduate, and undergraduate students) as well as our positionalities across womanhood. We offer implications for other faculty/instructors who wish to bring this sort of collaboration to their college classroom and teacher education.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.043 | 0.025 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.005 | 0.011 |
| 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".