Ocean Decade Challenge 10 underscores social dynamics in marine sciences as critical to transforming human–ocean relationships
Bibliographic record
Abstract
Abstract The United Nations Decade of Ocean Science for Sustainable Development (2021–2030) seeks to deliver “transformative ocean science solutions for sustainable development, connecting people and the ocean.” Yet, traditional scientific methods and data collection alone are insufficient to catalyze the societal change needed for ocean sustainability and equity. This article focuses on the vital role and significance of ocean literacy as central to operationalizing one of the foundational challenge areas within the Ocean Decade framework—Restoring society's relationship with the ocean (Challenge 10). It draws attention to the key insights in the recently published Challenge 10 White Paper and the broader Vision 2030 ambition-setting process. First, it highlights the need to shift beyond sustainable ocean management to include managing human behaviors that impact ocean health. Second, it reframes ocean literacy as a “societal outcome,” offering a unifying lens to capture contextualized human–ocean relationships, science-policy-society connections, and insights to bridge the knowledge-action gap. The authors present a novel figure integrating four key drivers and operational activities offering tangible pathways to strengthen and restore ocean-society relations.
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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 | 0.021 |
| 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 | 0.026 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".