Gathering Stakeholders: Education about the Salish Sea Ecosystem for Vancouver's Adult Residents
Bibliographic record
Abstract
"Education of the Salish Sea is a big pool of information waiting to be tapped into" (Vancouver environmental adult educator). Residents of Vancouver, British Columbia (BC), Canada live alongside one of the largest and most biologically diverse inland seas in the world, the Salish Sea. Although a rich variety of marine environmental adult education opportunities are offered in Vancouver, educators have found that attendance is low and programs generally attract repeat participants described as the “already converted”. Overall, there appears to be a disconnect between the capacity for this education in Vancouver and the number of people who currently attend existing programs. Education about marine ecosystems is critical for residents of coastal urban centres due to the impact that human activities have on the sea and estuarine areas; however, scholarly research in this area is scarce. This qualitative case study explored marine environmental adult education in the densely populated city of Vancouver, BC from the perspective of local stakeholders. An interview was conducted with one contact person from each of the following stakeholder groups: marine adult education planners, marine advocates, local First Nations' peoples, and the City of Vancouver. As well, a small sample of Vancouver residents who represented the curious and beginning adult learner participated in a focus group. Three themes emerged from the data analysis: (a) residents are connected to the Salish Sea but know little about it, (b) greater awareness has benefits for the ecosystem and residents, and (c) programs specifically designed for Vancouver's adult residents could engage citizens in marine environmental education. This presentation will identify directions for the development of marine environmental programs in Vancouver; suggest participant engagement strategies for both new and existing programs; mention discussion points and areas for further study; and provide an update on plans for a pilot program.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".