Development and expansion in the marine social sciences: Insights from the global community
Bibliographic record
Abstract
The importance of understanding the complexities of societal relationships with our global ocean, and how these influence sustainable management and effective, equitable governance, is crucial to addressing ocean challenges. Using established horizon scanning method, this paper explores current trends in marine social sciences through a survey of the global marine social science research and practitioner community (n = 106). We find that marine social sciences research is broad, covering themes relating to governance and decision-making, stakeholder participation and engagement, the socio-cultural dimensions of marine systems, ocean literacy, community-based and area-specific management, and the blue economy, and identify future research priorities highlighted by the community. Our results, however, suggest several barriers persist, including the relationship between marine social sciences and other disciplines, and the visibility and recognition of marine social sciences both internal and external to academia. Finally, the paper generates prospective thinking and highlights recommendations for future research and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".