Teaching a doctoral-level, interdisciplinary social studies course on critical social justice
Bibliographic record
Abstract
Purpose This article discusses the underlying teaching framework of relational cultural theory (RCT), as well as additional teaching practices used within a doctoral-level, interdisciplinary social studies course on critical social justice. Areas for future development are identified. Design/methodology/approach A research-engaged, conceptual report on practice was used to identify and integrate relevant scholarship for the purpose of formulating and analyzing teaching practices for this type of course, and to iteratively identify possible directions for future development. Findings RCT is a generative, underlying teaching framework for the interdisciplinary social study of critical social justice. Additional teaching practices including a community agreement to guide challenging discussions; participant-led presencing activities at the outset of classes; and, co-creation by participants of the content topics can be fruitfully embedded within RCT. Potential future development could include team-based, community-engaged, experiential term projects aimed at further deepening interdisciplinarity and civic engagement skills. Practical implications Practical guidance is provided on the use of RCT, community agreements, co-creation, presencing activities and Indigenous land acknowledgments or contemplations on Indigenous works. Social implications RCT can be used across different educational levels or contexts. Practices of co-creation, presencing and contemplation of Indigenous works are receiving increased consideration in diverse contexts. However, conventional grading procedures can be inconsistent with critical social justice, suggesting the need for research-engaged policy review. Originality/value This article responds to recent scholarly calls for discussion of teaching practices in the interdisciplinary, social study of critical social justice in post-secondary 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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".