Social Justice Pedagogies:Multidisciplinary Practices and Approaches
Bibliographic record
Abstract
Social Justice Pedagogies provides a diverse and wide perspective into making education more robust and useful in light of global injustices and new challenges posed by new media and communication practices, media manipulation, right-wing populism, climate crisis, and intersectional discriminations. Meant to inspire readers to see learning and teaching from a wider perspective of justice, inclusion, equity, and creativity, it argues that relational and mindful approaches to teaching and learning in specific contexts, settings, and place-based experiences are essential in how we determine the value of education. The book draws on contributions from scholars and experts who incorporate social justice into their teaching practices in different disciplines in universities across Canada, the US, and Europe. Social Justice Pedagogies uniquely presents a wide interdisciplinary perspective on social justice in education practices in order to speak to the ways in which we all want to make our research, our classrooms, and our institutions more just. It argues that pedagogy, and specifically teaching and learning, constitutes a process of building relationships between people and knowledge by fostering a learning community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.015 | 0.044 |
| Scholarly communication | 0.031 | 0.017 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".