Terminology in ecology and evolutionary biology disproportionately harms marginalized groups
Bibliographic record
Abstract
The discipline of ecology and evolutionary biology (EEB) has long grappled with issues of inclusivity and representation, particularly for individuals with systematically excluded and marginalized backgrounds or identities. For example, significant representation disparities still persist that disproportionately affect women and gender minorities; Black, Indigenous, and People of Color (BIPOC); individuals with disabilities; and people who are LGBTQIA+. Recent calls for action have urged the EEB community to directly address issues of representation, inclusion, justice, and equity. One aspect of this endeavor is to examine the use of EEB's discipline-specific language and terminology, which may have the potential to perpetuate unjust systems and isolate marginalized groups. Through a mixed-methods survey, we examined how members of the EEB community perceive discipline-specific terminology, including how they believe it can be harmful and which terms they identified as problematic. Of the 795 survey respondents, we found that almost half agreed that there are harmful terms in EEB and that many individuals from marginalized groups responded that they have been harmed by such terminology. Most of the terms identified as harmful relate to race, ethnicity, and immigration; sex and gender; geopolitical hierarchies; and historical violence. Our findings suggest there is an urgent need for EEB to confront and critically reassess its discipline-specific terminology. By identifying harmful terms and their impacts, our study represents a crucial first step toward dismantling deeply rooted exclusionary structures in EEB. We encourage individuals, communities, and institutions to use these findings to reevaluate language used in disciplinary research, teaching and mentoring, manuscripts, and professional societies. Rectifying current harms in EEB will help promote a more just and inclusive discipline.
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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 | 0.000 | 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) | 0.001 | 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".