The Many Currents of Ocean Literacy: A Case Study of Ocean Wise Programming
Bibliographic record
Abstract
Recent scientific studies demonstrate conclusively that our planet faces an ocean crisis and efforts to mitigate this crisis should be addressed urgently. As many species are lost to extinction, conservation steps need to be taken and are indicated by targets such as those outlined by the 2030 Agenda for Sustainability and Development. Similarly, a clear agenda for ocean education should be encouraged as the first step toward broader conservation goals. Recently, the concept of ocean literacy has been described as a way to help communities and individuals develop a more holistic understanding of their influences on the ocean and the ocean’s influences on their lives. Still, ocean literacy has not yet been fully enacted in the K–12 curricula in Canada, and many environmental education programs are taking the lead to provide program participants with a broader understanding of the term. In this study, we provide a broad overview of ocean literacy initiatives as enacted by the Ocean Wise NGO. We examine how these have influenced the diffusion of ocean literacy in British Columbia. In our paper, we include a case study highlighting the diversity of Ocean Wise programs to provide a broad view on activities from the perspective of program participants. We selected a range of education programs for data collection, including school visits to the Vancouver Aquarium, off-site mobile programming (with AquaVan), and teacher professional development programs, both on-site and via an online learning platform. We explore what each initiative offers students with regard to connection to the ocean. Through an instrumental case study design, we combine qualitative approaches with observations, focus groups, and interviews to describe many currents of ocean literacy flowing from Ocean Wise and its broad and diverse ocean literacy programming.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".