Coalition Building and Discomfort as Pedagogical Strategies
Bibliographic record
Abstract
Innovative design solutions come from inclusive and diverse design teams (Page 2008). In this paper, I reflect on how such insights can be used in developing pedagogical approaches that use coalition building, knowledge translation between disciplines, and pedagogies of discomfort to foreground implicit biases impacting architectural practice and education. Based on interviews with educators thinking about the built environment, as well as Kevin Kumashiro’s (2002) anti-oppressive education framework and Megan Boler’s (1999) notion of a pedagogy of discomfort, and building on examples from queer and feminist educators, I suggest in this paper that the disruptive use of feelings and emotions in architectural education can prepare students for more collaborative and inclusive practices. Such discussions allow students to understand the impact of biases but also to think about tools to acknowledge and challenge inequity in the design of the built environment and in the design professions themselves. Cross-disciplinary collaboration, at both the students and the educators level, can also create opportunities for coalition building, particularly in contexts where a limited number of faculty are explicitly discussing race, gender, disability, class, sexuality, or ethnicity in their teaching. Faculty members with diverse individual self-identifications can multiply their impact by working together to tackle the intersecting ways in which minoritized experiences are pushed aside in mainstream architecture discourses and education. They can also foreground their combined experiences as positive role models to create a constructive learning environment to address these issues, both within universities and directly in the community.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".