Facing the Limitations of Teaching and Learning Committed to Solidarity and Social Justice: A Feminist Approach
Bibliographic record
Abstract
This study examines how 52 graduate students enrolled in a social justice program in a large urban university in Canada relate to others in and outside the college classroom. Using a method involving creative student audio productions and self-reflections, the study finds that despite extensive learning about others and engaging with postcolonial, race, feminist, Indigenous and other critical theories and knowledges in the program, both racially privileged students and those who are members of disadvantaged and marginalized social groups enact relations extending power structures imagined as “solidarity.” These relations have important gender aspects and are shaped by broader historical forces, such as racism and colonization, as well as contemporary aspects of Canadian academia that have not been studied fully in relation to social justice education. These findings call for social justice curriculum and pedagogies that move learners from thinking critically and feeling empathy for others to behaving ethically and standing in political togetherness with those others as equals. The study suggests that transnational women and feminist political practices of solidarity mobilizing cross-racial and cross-national women’s movements in the 1970s, 1980s and 1990s present valuable pedagogies that could position college students for such relating.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.086 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".