One Classroom at a Time: How Better Teaching Can Make College More Equitable
Bibliographic record
Abstract
David Gooblar’s “One Classroom at a Time” is a practice-centred book on the pressing discussion on equity, diversity, and inclusion in higher education. The book opens with a powerful statement: “the majority of our existing curricula are designed for imaginary students” (Gooblar, 2025, p. 17). This notion of the “imaginary student,” described as ones who populate elite institutions and dominate the discussion on curriculum design and pedagogy, is deeply anchored throughout the book. In response to the long-standing archetype of fragmentation, competition, gatekeeping, and favouritism that dominate curricula and pedagogy, this thought-provoking book draws on evidence from psychological studies and historical analysis to challenge the practices of disembeddedness. The increasingly diverse student population in higher education underscores the need for educators, staff, and administrators to shift from a deep-rooted archetype to an identity-conscious pedagogy. With a writing style that is vivid, critical, accessible, and well informed by research and classroom experience, the author creates a book suitable for all audiences, providing actionable classroom suggestions, toolkits, and future recommendations. Amid today’s complex political climate, this book is an unapologetic defence of advocacy efforts for a more equitable 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".