Barriers and Strategies by White Faculty Who Incorporate Anti-Racist Pedagogy
Bibliographic record
Abstract
This study focused on the experiences of White faculty who incorporate an anti-racist framework into their college classrooms. The participants shared about the challenges of incorporating anti-racist pedagogy into their classrooms due to both perceived personal and institutional barriers. These participants perceived personal barriers stemming from an internalized struggle of understanding their own White identity while also struggling to be viewed as anti-racist educators by colleagues of color. These faculty participants also shared about perceived professional barriers which included the pressure to obtain tenure, perceived loss of control in the classroom by the students, and anti-racist work being disregarded by individuals in positions of institutional power. Through the use of narrative inquiry, five researchersexploredthe personal and professional barriers faced by White faculty engaging in anti-racist educational practices in the college classroom. The study included17 faculty participants teaching at predominately White private and public colleges and universities throughout the United States who teach in various academic disciplines. Findings revealed the ongoingbarriers in teaching anti-racism ideals and the discussion provides strategies and an emerging modelforincorporating intentional anti-racist pedagogy into the classroom.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".