“Where do I even start?” Recommendations for faculty diversifying syllabi in ecology, evolution, and the life sciences
Bibliographic record
Abstract
Diversifying curricula is of increasing interest in higher education, including in ecology and evolution and allied fields. Yet, many educators may not know where to start. Here we provide a framework for meeting standard curriculum goals while enacting anti-racist and anti-colonial syllabi that is grounded in the development of a sustainable network of educators. In addition to highlighting this professional learning process and sharing the list of resources our group has developed, we provide suggestions to help educators highlight contributions of minoritized groups, explore multiple ways of knowing, and perform critical assessments of foundational views of life and environmental science fields. We further discuss the key classroom dynamics that affect the success of such anti-racist and anti-colonial initiatives. The retention and success of minoritized students in ecology and evolution depends on whether we address injustices in our fields. Our hope is that our fellow educators will use this paper to catalyze their own efforts to diversify their courses.
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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 | 0.030 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.059 | 0.026 |
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".