Building a Coalition and National Strategy to Advance Ocean Literacy in Canada–Lessons Learned, Adaptations, and Next Steps
Bibliographic record
Abstract
Canada has the longest coastline in the world, stretching over 243,000 kilometres. Representing one-fifth of the world’s freshwater, Canada has over two million lakes and more than 8,500 rivers, all of which eventually drain into one of five ocean basins. Amidst such abundance, there are various considerations and lived experiences that shape people in Canada’s relationship with the ocean, including regional (e.g., coastal, inland), cultural (e.g., Inuit, First Nation, Métis, settler, newcomer), linguistic (e.g., English, French, Indigenous languages), and other important perspectives related to education, food security, livelihoods, governance, politics, economics, and more. Understanding such varying relationships with the ocean (and water) reflects the essence of ocean literacy. The Canadian Ocean Literacy Coalition (COLC), a community-driven alliance of over 400 regional and national organizations, networks, institutions, communities, and individuals, led a national study (2019-2020) that was guided by three questions: 1) What is the current state of ocean literacy in Canada? 2) What are the current gaps, barriers, and enablers? 3) What are the key recommendations to advance ocean literacy in Canada? The research findings led to the co-development of Land, Water, Ocean, Us: Canadian Ocean Literacy Strategy (March 2021), making Canada the first country in the world with a national strategy and providing a collaborative framework for action at the start of the Ocean Decade. Now, three years into the Strategy implementation, COLC’s work has led to the launch of several national and international joint action initiatives, such as Ocean Week Canada and the global Ocean Literacy Research Community. A National Strategy Impact Measurement Program has been developed to map, monitor, and evaluate progress and impact key initiatives that are advancing ocean literacy in Canada. This presentation will highlight the collaborative design approach and the intrinsic value of building a bottom-up National Strategy. Key insights and considerations on mobilizing collective action across regions, sectors, and scales while respecting regional and cultural diversity will be shared. Finally, the session will conclude with reflections on the Coalition model and Strategy framework, as well as results-based lessons learned, adaptations, and next steps to advance ocean literacy in Canada and globally.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".