Development of an Equity, Diversity, and Inclusion Curriculum Initiative for Undergraduate STEM Students
Bibliographic record
Abstract
Equity, diversity, and inclusion (EDI) gaps persist in science, technology, engineering, and math (STEM) fields, as demonstrated by the discrimination, stereotyping, and inequities that historically and persistently marginalized groups face. Recognition of this gap led a transdisciplinary team to develop foundational-level e-learning modules, titled Foundations for Inclusive and Respectful Engagement (FIRE) on EDI capacities to be delivered in STEM undergraduate classes at the University of British Columbia’s Okanagan campus. FIRE consists of online, asynchronous, self-study modules delivered through the learning management system, Canvas. Feedback from pilot testing the FIRE modules has demonstrated that STEM students find the modules to be relevant and beneficial. Throughout the development of FIRE, we learned the importance of aligning the course with our institution’s values, working in a transdisciplinary team, and revising iteratively. This documentation of the development and preliminary feasibility of the FIRE modules aims to assist other institutions or organizations who are in the process of developing their own EDI teaching and learning materials.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".