Bibliographic record
Abstract
Promoting Inclusion and Justice in University Teaching offers a theoretical and practical contribution to ongoing debates concerning why and how we need to expand the goals of education in an increasingly diverse academia to enhance inclusivity and equity. It integrates a wide range of well-designed teaching activities grounded in the principles of transformative pedagogy into university settings to connect in-class teaching to social justice demands. Expert contributors employ an array of constructivist and critical epistemological approaches, including indigenous, anti-racist, decolonial, feminist, and intersectional viewpoints, to conceptualize and elucidate their proposed pedagogical frameworks. Chapters demonstrate why and how theoretical and practical principles of transformative pedagogy can respond to the goal of making higher education classrooms not only more inclusive, but also transformative and empowering spaces for teachers and learners alike. The book addresses a crucial gap in higher education, offering a comprehensive toolkit tailored to both undergraduate and advanced students which encourages learners to create a positive social change. Combining practical teaching methods and grounded pedagogical theory, this book will be a highly beneficial read for scholars and researchers teaching in a variety of fields in humanities and social sciences as well as those specializing in teaching and learning, curriculum and pedagogy, and teaching methods in a variety of disciplines. Its blueprint for increasing inclusion and equity in teaching will also benefit professionals and practitioners engaged in formal and non-formal education settings with adults and youth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".