Ecology and Evolutionary Biology Must Make Room for BIPOC Scholars
Bibliographic record
Abstract
Research in ecology and evolutionary biology (EEB) plays a key role in understanding and intervening in our current environmental and climate crisis. Although anthropogenic stressors and climate change continue to disproportionately affect Black, Indigenous, and people of colour (BIPOC) individuals, their valuable scientific voices are shockingly underrepresented within EEB. To underscore this problem, we present a case study on EEB PhD graduates in the US (1994-2018), which illustrates that BIPOC scholars are significantly underrepresented in their cohorts. We recommend key steps that the EEB Academy should take to increase representation of BIPOC scholars in EEB, including anti-racism education and practice, increased funding opportunities, integration of diverse cultural perspectives, and a community-minded shift in PhDs. Importantly, this advice is directed at those who wield power in the Academy (e.g., funding agencies, societies, institutions, departments, and faculty), rather than BIPOC scholars already struggling against inequitable frameworks in EEB.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".