‘Failing well’ in teaching about race, racism and white supremacy. An interview with Stephen Brookfield
Bibliographic record
Abstract
Since embarking on his educational journey in 1970, Professor Stephen Brookfield has worked across various international settings, including England, Canada, Australia, and the United States. His experience spans a diverse range of environments, from adult and community education to prestigious higher education institutions like Harvard University and Columbia University. Central to his mission is aiding adults in critically examining prevailing ideologies they have absorbed. To advance this goal, Professor Brookfield has authored, co-authored, and edited 21 books encompassing topics such as adult learning, teaching methodologies, critical thinking, discussion techniques, critical theory, and anti-racist teaching. Expanding upon our previous dialogues with Stephen Brookfield in the Journal of Applied Learning & Teaching (Brookfield et al., 2019, 2022) and complementing the reviews of his recent publications (Rudolph, 2019, 2020, 2022; Waring, 2024), this interview delves deeper into the themes explored in our recent book on Teaching well (Brookfield et al., 2024). This extensive conversation significantly elaborates on Chapter 9 of the book (Brookfield et al., 2024) and investigates the intricate, emotionally charged, and political project of teaching about race. In this expansive discussion, we explore Stephen Brookfield’s personal evolution from harbouring racist beliefs in his youth to embracing and contributing to Critical Race Theory (CRT), a journey marked by a decade of introspection and scholarly exploration, culminating in several key publications (Sheared et al., 2010; Brookfield & Associates, 2018; Brookfield & Hess, 2021). The conversation illuminates fundamental concepts such as race, racism, and white supremacy, recontextualising racism as a systemic issue rather than an individual failing. Racism is depersonalised and an endemic system of exclusion. We discuss it in the context of an intersectional analysis that acknowledges the interconnectedness of various forms of oppression, including classism, sexism, and ableism. A significant focus is placed on racism within the higher education sector. Brookfield shares insights from his extensive experience in conducting antiracist workshops for students, faculty, and organisations. He challenges the notion of the ‘good white people’ and advocates for a continuous, imperfect journey towards antiracism, where ‘failing well’ can be regarded as a good outcome.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.017 |
| 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".