Enhancing equity, diversity, inclusion, indigeneity and accessibility (EDIIA) across undergraduate immunology curriculum 2435
Bibliographic record
Abstract
Abstract Description The field of Immunology is inherently collaborative, with both teachers and learners possessing diverse needs and experiences. Incorporating themes of Equity, Diversity, Inclusion, Indigeneity, and Accessibility (EDIIA) within academic curricula allows for the fostering of supportive and healthy teaching and learning environments for enhancement of student learning. The Department of Immunology at the University of Toronto completed environmental scans of EDIIA topics and discussions across undergraduate departmental course offerings. Stakeholder consultations were conducted to review lecture and tutorial materials, course activities and course assessments. Centralized, online department-wide resources were created, including an EDIIA handbook and PowerPoint presentations for inclusion of accessibility tools in teaching. In addition, EDIIA themes were developed with current and prospective EDIIA topics and organized in a curriculum map. The curriculum map revealed opportunities to enhance EDIIA content within each existing course, and each course instructor received course-specific evidence and resources supporting EDIIA themes for teaching in their course. In this presentation, we will focus on the model for our approach, lessons learned, project outcomes, and future goals. Taken together, the creation, centralization, and communication of course-specific resources could facilitate both student and instructor engagement with EDIIA goals in the field of Immunology. Topic Categories Immunology Education and Communication (EDU)
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".