Ecology and evolutionary biology must elevate BIPOC scholars
Bibliographic record
Abstract
Black, Indigenous and people of colour (BIPOC) individuals are disproportionately impacted by the negative consequences of our ongoing environmental and climate crises, yet their valuable scientific voices are shockingly underrepresented within the fields of Ecology and Evolutionary Biology (EEB). As early-career BIPOC EEB researchers, we recognise the key role that our fields play in understanding and mitigating the effects of our ongoing global crises, and are concerned about the lack of diversity we see among our own EEB cohorts and mentors. We present this piece as a call to action for the EEB Academy, drawing on our own experiences and the literature to suggest steps the Academy must take to increase representation of and equity for BIPOC graduate scholars in EEB. We synthesise these steps into four actionable ideas: anti-racism education and practice, increased funding opportunities, integration of diverse cultural perspectives and a community-minded shift in PhDs. Importantly, this advice is specifically 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".