Ocean literacy research community: co-identifying gaps and priorities to advance the UN Ocean Decade
Bibliographic record
Abstract
Introduction The overarching goal of the UN Ocean Decade is to “change humanity's relationship with the ocean.” While this may be a challenge, it is, at the same time, a once in a generation opportunity. How can 8 billion people, including those who don't live near coastal areas, be inspired to value and care for the ocean? This is the essence of ocean literacy, and the driver of ocean literacy research (OLR). Methods In 2021, we began a research initiative to co-create a global OLR agenda by the developing OLR community, to better understand existing research themes, gaps, future priorities, actions, and impacts of ocean literacy initiatives. To deliver this, a series of virtual workshops – with the first taking place as part of the UN Ocean Decade Laboratories – was complemented by a participatory methodology using digital survey and mapping tools for crowdsourced collaboration. Results and discussion Through this process, four initial OLR priorities were identified, including measuring ocean literacy, the role of ocean literacy as a policy mechanism, and alignment of OLR with climate change and the blue economy agendas. Finally, a working definition of OLR was developed to further guide OLR priorities for the UN Ocean Decade and beyond.
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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 | 0.184 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".