Making aquatic sciences more Diverse, Equitable, Inclusive, and Accessible: Perspectives on how individuals can take action in their professional practice
Bibliographic record
Abstract
Achieving Diversity, Equity, Inclusion, and Accessibility (DEIA) in the aquatic sciences has been a chronic challenge, and while recent progress has been made, shifting political and institutional landscapes increasingly jeopardize these crucial efforts. To highlight strategies on how to continue to support DEIA initiatives, the Society of Canadian Aquatic Sciences and the International Association for Great Lakes Research co-hosted a webinar with diverse panelists entitled Making Aquatic Science Spaces More Equitable, Diverse, Inclusive & Accessible: A Panel Discussion. Building on the webinar, we synthesize eight actions individuals in aquatic sciences can take to uphold DEIA values and dismantle barriers: (1) make safety front of mind; (2) embrace complexity and intersectionality; (3) be proactively compassionate and inclusive; (4) identify and remove barriers; (5) engage non-scientific audiences in outreach and public dialogue; (6) be ready to make mistakes and learn from them; (7) be prepared to challenge misinformation; and (8) keep dialogue open about DEIA realities. We have also included narratives which highlight lived experiences of the panelists and how these actions have been implemented in the real world. In the face of growing political and institutional challenges, advancing DEIA in aquatic sciences will increasingly rely on grassroots action, sustained by individual commitment to building a more inclusive and just community.
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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 | 0.053 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.066 |
| Scholarly communication | 0.031 | 0.022 |
| Open science | 0.003 | 0.037 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".