Developing an Interdisciplinary Inquiry Course on Global Justice: An Inquiry-Informed, Cross-Campus, Collaborative Approach
Bibliographic record
Abstract
Higher-education institutions have an increasing responsibility to foster “global citizenship,” enabling students to recognize injustice and pursue equity. As a first step to creating a larger “hub” for global justice, McMaster University set out to develop an interdisciplinary course on the topic. With high-level institutional support, a cross-campus, interdisciplinary course design team was formed to further investigate effective pedagogy. Inquiry-based learning (IBL) was considered a foundation for other learning strategies within the course because of its evidenced ability to instigate a process of “learning by doing,” requiring students to both self-direct their education and develop their capacities as independent learners. To provide a further evidence base, a student member of the committee also conducted a pan-Ontario study surveying relevant instructors on successful global justice pedagogies. Collectively, these findings were integrated to inform the development of “Global Justice Inquiry,” which is characterized by its small course size, open-inquiry style, and engagement of alumni, community partners, and faculty from across campus. This chapter details the process followed to develop this course, presenting it as a model that might be helpful to others looking to develop interdisciplinary inquiry offerings.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".