Promoting socially responsible governance of new marine climate intervention
Bibliographic record
Abstract
Novel climate interventions are proliferating and upscaling in marine systems. However, how social impacts are managed remains unclear. We combine a global survey of intervention actors, interviews with best-practice leaders, and policy analysis to assess whether and how social responsibility is considered when proposing, testing, and/or implementing 76 marine climate interventions worldwide. We find that technical feasibility trumps social considerations. Feasibility assessments predominantly rely on biophysical data (63%), with 54% either not using social data or relying on spatial marine use data as the only social data source. Where public deliberation opportunities are available (61%), most are via formal regulatory channels (54%), with only 15% offering more inclusive engagement. Best-practice leaders confirm low organizational competency around social impact. Social responsibility is rarely mandated by governments and instead relies on voluntary initiation by emerging best-practice leaders. Extension and codification of best practices are urgently required for socially responsible governance of new marine climate interventions.
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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 | 0.094 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".