Emotions for Sustainable Oceans: Implications for Marine Conservation
Bibliographic record
Abstract
This essay examines emotions as a critical, yet underutilized, dimension in marine conservation and ocean sustainability science. Drawing on cognitive neuroscience, social psychology, human geography, and political ecology, we argue that integrating emotional dimensions into research, policy, and practice can enhance both understanding and action toward marine conservation and ocean sustainability. We conceptualize emotions, and explore their experiential and functional implications in marine contexts. Using targeted case examples and theories, we identify both opportunities and challenges for applying emotional insights in research, policy, and practice, including barriers posed by dominant rationality models of human decision-making. We present intellectual pathways as well as research, methodological and policy agendas to integrate emotions into marine conservation research and strategies. Our analysis responds to gaps in the literature and provides actionable recommendations for researchers, policymakers, and practitioners during the UN Decade of Ocean Science for Sustainable Development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".