Incorporating equity, diversity, and inclusion in science: Lessons learned from an undergraduate seminar
Bibliographic record
Abstract
Abstract Questions of equity, diversity, and inclusion in the sciences have taken center stage in light of the COVID‐19 pandemic and Black Lives Matter movement of 2020. This paper focuses on the experiences of academics engaging in such work, particularly in their roles as educators, by sharing two of the authors' experiences introducing equity, diversity, and inclusion initiatives in a first‐year science course at a Canadian university. Using critical research methodologies like narrative inquiry and memory work, we look at three separate instances where complex personal, institutional and course attributes fostered, allowed, or hindered efforts to bring these initiatives into the classroom. We consider how problematic incidents and obstacles relating to the organization of content on equity, diversity, and inclusion in science cropped up during the process, how they were perceived and handled in the moment, as well as the authors' reflections, takeaways, and lessons learned from the experience. These stories suggest that efforts to center discussions about equity, diversity, and inclusion in undergraduate science classrooms can be unpredictable and complex, particularly at the day‐to‐day level; this is especially the case when handling subtler microaggressions rather than clear instances of discrimination or harassment. Our study points to the importance of creating a more permanent institutional memory for initiatives that outlive those who initiated and organized them, so that they become embedded within the culture of a course or department.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".