Social Considerations and Best Practices for Engaging Publics on Ocean Alkalinity Enhancement
Bibliographic record
Abstract
Abstract. In the wake of numerous propositions to trial, test or up-scale ocean alkalinity enhancement (OAE) for carbon dioxide removal (CDR), multiple social considerations have begun to be identified. To ensure that OAE research is responsible (is attentive to societal priorities) and successful (does not prematurely engender widespread social rejection), it will be critical to understand how OAE might be perceived as risky or controversial, and under what conditions it might be regarded by relevant social groups as most worthy of exploration. To facilitate the answering of these questions, this chapter: (1) characterizes what is known to date about public perceptions of OAE; (2) provides methodological suggestions on how to conduct social science research and public engagement to accompany OAE field research, and; (3) addresses how knowledge gained from social research and public engagement on OAE can be integrated into ongoing scientific, siting, and communications work.
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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 | 0.102 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.010 | 0.009 |
| 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".