Ocean Literacy for Ocean Sustainability: Reflections From Australia
Bibliographic record
Abstract
Ensuring a sustainable future for the global ocean requires meaningful dialogue and engagement with society. Around the world, efforts to engage and collaborate with society increasingly emphasise ocean literacy as a potential tool for engaging and educating people on ocean issues. A conceptual measure of people’s awareness, attitudes, and behaviours towards the ocean, ocean literacy has been highlighted as a key objective in recent ocean sustainability agreements and initiatives, including the UN Decade of Ocean Science for Sustainable Development. In Australia, research and applied interest in ocean literacy is burgeoning. It is therefore timely to take stock and explore recent work that may inform future pathways towards supporting and engaging society in achieving ocean sustainability. Here, we explore examples of ocean literacy research and practice in Australia, to develop prospective thinking on inter/transdisciplinary approaches for advancing ocean literacy under sustainability objectives. In doing so, we anticipate the next steps for progressing ocean literacy in the Australian context, including supporting ocean learning and education, engaging communities at all levels, fostering cross‐sector collaboration on connecting people to the ocean, and building strong and actionable policy and funding frameworks to ensure long‐term impact. We emphasise the need to collaboratively develop a national ocean literacy strategy to guide and structure these efforts and to establish an Australian ocean literacy coalition to facilitate research, cross‐sector collaboration, and implementation in practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".