Five social science intervention areas for ocean sustainability initiatives
Bibliographic record
Abstract
Abstract Ocean sustainability initiatives – in research, policy, management and development – will be more effective in delivering comprehensive benefits when they proactively engage with, invest in and use social knowledge. We synthesize five intervention areas for social engagement and collaboration with marine social scientists, and in doing so we appeal to all ocean science disciplines and non-academics working in ocean initiatives in industry, government, funding agencies and civil society. The five social intervention areas are: (1) Using ethics to guide decision-making, (2) Improving governance, (3) Aligning human behavior with goals and values, (4) Addressing impacts on people, and (5) Building transdisciplinary partnerships and co-producing sustainability transformation pathways. These focal areas can guide the four phases of most ocean sustainability initiatives (Intention, Design, Implementation, Evaluation) to improve social benefits and avoid harm. Early integration of social knowledge from the five areas during intention setting and design phases offers the deepest potential for delivering benefits. Later stage collaborations can leverage opportunities in existing projects to reflect and learn while improving impact assessments, transparency and reporting for future activities.
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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 | 0.036 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".