Experiences of and support for black women in ecology, evolution, and marine science
Bibliographic record
Abstract
Systemic racism and sexism are well documented in ecology, evolution, and marine science. To combat this, institutions are making concerted efforts to recruit more diverse people by focusing on the recruitment of Black people. However, despite these initiatives, white supremacy culture still prevails. The retention of Black people in ecology, evolution, and marine science has not increased in the ways that were hoped for. This is particularly true for Black women, who struggle to find a safe working environment that values their contributions and allows them to openly celebrate their own culture and identity. In this perspective article, we discuss the challenges that Black women face every day, and the needs of Black women to thrive in ecology, evolution, and marine science. We have written this directly to Black women and provide information on not only our challenges, but our stories. However, readers of all identities are welcome to listen and examine their role in perpetuating systemic racism and sexism. Lastly, we discuss support mechanisms for navigating ecology, evolution, and marine science spaces so that Black women can thrive.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".