The Ocean & Society Survey: A Global Tool for Understanding People–Ocean Connections and Mobilizing Ocean Action
Bibliographic record
Abstract
Recent years have seen calls for improved ways of assessing and understanding ocean literacy across a range of contexts. This article presents collaborative advances toward these ends on a global scale, through the co-creation of the Ocean & Society Survey. This Survey—based on national surveys in Canada, Brazil, and the UK—and the collaboration of 20 core partners, aims to capture diverse people–ocean connections. The article outlines the Ocean & Society Survey’s objectives to: (a) strengthen people–ocean relationships by exploring how people understand, value, and/or engage with the ocean; (b) guide pathways of engagement by identifying behavioural motivations, barriers, and enablers; (c) generate insights to inform targeted, audience-specific ocean communications campaigns; (d) demonstrate the value of transdisciplinary partnerships; and (e) better understand what influences peoples’ interests and concerns about the ocean, alongside the willingness and capacity to take action and make informed decisions. The article presents the co-design process of the global tool. In particular, it outlines the analytical approach using thematic, dimensional, and metric indices to compile a question set that can be used to achieve the above objectives by comparing public ocean perceptions over time and across regions. It discusses processes of external review, piloting, and launch in the lead-up to the third UN Ocean Conference, and the projected trajectory until 2030.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".