Powerful teaching, the paradox of empowerment and the powers of Foucault. An interview with Professor Stephen Brookfield
Bibliographic record
Abstract
This interview follows up on a previous interview with Stephen Brookfield in the Journal of Applied Learning & Teaching (Brookfield et al., 2019). It was conducted as part of an ongoing book project with the working title Teaching well that the authors are involved in. This interview may be seen as a teaser for the book that is planned to be published in 2023. It constitutes an extended version of one out of 13 planned chapters and focuses on how power shows up in higher education classrooms. Classrooms are never power-free zones. Every learning environment contains student-to-student and student-to-teacher power dynamics. We discuss various influences on Stephen Brookfield’s conceptual understanding of power, especially Michel Foucault’s concepts of sovereign, disciplinary and bio-power and their applicability to education. In this context, we explore similarities between prisons and schools, the metaphor of the panopticon, and the continued relevance of bio-power during the current pandemic. The democratic practice of discussion groups is questioned (despite Stephen Brookfield’s personal preference of that modality) and the lecture is reinstated as one of several useful modalities. We then arrive at Brookfield’s concept of powerful teaching & learning and how teachers can exercise their power in ethical, productive and responsible ways. The interview ends with Brookfield’s advice on institutional criticism. Since beginning his teaching career in 1970, Stephen Brookfield has worked in England, Canada, Australia, and the U.S., teaching in a variety of adult, community, organisational and higher education settings (the latter include Harvard University and Columbia University). In his endeavour to help adults learn to think critically about the dominant ideologies they have internalised, Professor Brookfield has written, co-written or edited 20 books on adult learning and teaching, critical thinking, discussion methods, critical theory and teaching 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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| 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".