Making ocean literacy inclusive and accessible
Bibliographic record
Abstract
Engagement in marine science has historically been the privilege of a small number of people with access to higher education, specialised equipment and research funding. Such constraints have often limited public engagement and may have slowed the uptake of ocean science into environmental policy. Recognition of this disconnect has spurred a growing movement to promote ocean literacy, defined as one’s individual understanding of how the ocean affects people and how people affect the ocean. Over the last 2 decades, this concept has gained significant traction in marine biology and environmental education circles and now plays a prominent role in the UN’s Decade of Ocean Science for Sustainable Development (2021-2030). Here, we argue that the ocean literacy agenda has largely been shaped and discussed by marine scientists and educators but needs to be expanded to a much larger constituency to be more effective, accessible and inclusive. We discuss diverse cultural settings from around the world and provide examples of indigenous, spiritual, art, ocean user and other groups that are already deeply engaged with the ocean and could provide a variety of perspectives to enrich the ocean literacy concept beyond an understanding of marine science. We suggest that such inclusiveness could remove the historic barriers that have surrounded the field, transform our collective awareness of and relationship with the ocean and help support ongoing efforts to restore marine biodiversity.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".