Six Years of Adult Education about the "Wonders of the Salish Sea"
Bibliographic record
Abstract
The "Wonders of the Salish Sea" (WSS) is an environmental education program designed to connect residents to the Salish Sea ecosystem through community-based, awe-inspiring education. The program aims to create a community of citizens who love, care for, and want to protect the Salish Sea; and to provide an opportunity for local scientists, naturalists, environmentalists, and enthusiasts to share their passion and expertise with the general public. Started in 2016 in Vancouver BC, WSS fills a gap in education by meeting the needs of beginning youth and adult learners who want to gain an in-depth understanding of the ecosystem in which they live. The program moved to a virtual format in 2021, making it accessible to all residents of the Salish Sea and beyond. WSS is held over a 4 - 5 week period annually in the spring. Attendance has grown every year with many returnees. The response from stakeholders has been overwhelmingly positive. For example, participants have said: "It has greatly increased my desire to preserve the Salish Sea and it's inhabitants," "I think the ripple effects of this kind of education cannot be tabulated," and "I've always been careful about what I advocate for but since I took this course I've written 3 letters." This poster will highlight the program model, participant data, outcomes, stakeholder feedback, spin-off initiatives, and plans for the future.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".