Continuing the work of respectful engagement: AFS 2025 and Indigenous partnerships in San Antonio
Bibliographic record
Abstract
Conversations around the need for greater Indigenous inclusion at the American Fisheries Society (AFS) meetings have been ongoing for several years. During the 2023 AFS Annual Meeting in Grand Rapids, Michigan, conversations with Indigenous attendees further underscored that our professional society must do more to engage with Indigenous members and the Tribes or Nations in the places where our meetings occur. In response, the Respectful Meetings Working Group (RMWG) was formed in 2024 by a group of dedicated volunteers (Cannon et al., 2024). The group’s mission is rooted in two guiding principles: to be better guests on Indigenous lands and to welcome, value, and celebrate Indigenous Peoples, and knowledge systems at AFS meetings. In 2024, the RMWG partnered with Kua`āina Ulu ‘Auamo (kuahawaii.org), a Native Hawaiian-led organization, to organize service-learning projects, highlight Indigenous speakers, and support Native Hawaiian businesses during the AFS Annual Meeting in Honolulu, Hawai‘i. The group raised over US$66,000 to support Indigenous participation, hosted a networking event attended by nearly 100 Indigenous individuals and allies, coordinated service events involving hundreds of participants, and provided safe spaces and culturally relevant programming. These efforts were grounded in the belief that AFS meetings should reflect inclusion, reciprocity, and respect.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.016 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 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".