Advancing Public Ocean Perceptions Research: a guiding approach to strengthen collaboration for ocean health
Bibliographic record
Abstract
The United Nations Decade of Ocean Science for Sustainable Development (2021–2030) emphasizes advancing our understanding of the ocean and promoting sustainable practices to ensure ocean health and interconnected well-being. As we approach the halfway mark, achieving this agenda requires informed and actionable collaboration among professionals involved in ocean science, management, conservation, industry, communication and education, as well as interested and/or affected groups, guided by science–policy– society linkages. Prioritizing the role of society can help to motivate and enable the restoration of people–ocean relationships. Understanding public ocean perceptions is crucial for grounding such efforts in place-based relevance. This paper discusses the role of Public Ocean Perceptions Research (POPR), a form of ocean literacy research, in this context. We analyse five pan-Canadian POPR surveys, each with specific ocean-focused objectives, to highlight their potential to contribute to ocean–human health. The surveys consistently reflect some themes (e.g., attitudes and behaviours), while lacking others (e.g., ocean solutions). Particular attention on where a marine social science lens is, or may be reflected, can help to inform the design and analysis of future POPR studies to better understand how people understand, value, and care for the ocean. We position how arising themes and ocean literacy dimensions can be used in ongoing research efforts, to inform marine conservation and broader ocean science policy.
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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 | 0.169 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.023 | 0.030 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.006 | 0.036 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 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".