Incorporating equity, diversity, and inclusion in science: Lessons learned from an undergraduate seminar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.025 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it