Fostering equity, diversity, and inclusion in large, first‐year classes: Using reflective practice questions to promote universal design for learning in ecology and evolution lessons
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
Instructors can deliberately design for equity, diversity, and inclusion, including for large first-year classes, and now instructors have added challenges given COVID-19. Our paper explores the question: How do we integrate equity, diversity, and inclusion and universal design for learning (UDL) into first-year, undergraduate ecology and evolution introductory lessons given the COVID-19 pandemic? Given the large field exploring equity, diversity, and inclusion, we chose to focus on developing reflective practice question rubrics for before, during, and after lessons to encourage UDL for instructors, teaching assistants, and learners. We conducted a focus group within our team and discussed ideas related to online learning, including related pitfalls and solutions. Lastly, we created a figure to illustrate ideas and end with a general discussion. Our reflective practice questions for UDL rubrics, figure, focus group, and discussion aim to increase positive action for equity, diversity, and inclusion in the classroom and beyond.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.006 |
| 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