Next steps in dismantling discrimination: Lessons from ecology and conservation science
Bibliographic record
Abstract
Abstract Ecology, conservation, and other scientific disciplines have histories built on the oppression of marginalized groups of people. Modern day discrimination continues in these fields and there is renewed interest in dismantling these system of oppression. In this paper, we offer some examples of historical events which have shaped the field and argue that reckoning with colonial histories is part of the process to dismantle discrimination and achieve equity and inclusion. We discuss ways forward including incorporating different knowledge systems and reflecting on one's own biases and privilege. To truly achieve fields of science which are just, diverse, and equitable will be one of our greatest challenges, but one that is necessary to protect our environment, an endeavor which cannot be detangled from societal injustices.
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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 | 0.039 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".