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Record W4389052708 · doi:10.5194/sp-2-oae2023-11-2023

Social considerations and best practices to apply to engaging publics on ocean alkalinity enhancement

2023· article· en· W4389052708 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueState of the Planet · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsUniversity of British Columbia
FundersPrince Albert II of Monaco FoundationClimateWorks FoundationPacific Institute for Climate Solutions
KeywordsAlkalinityPerceptionPublic relationsPublicsSociologyField (mathematics)Marine researchPublic engagementPolitical sciencePsychologyOceanography

Abstract

fetched live from OpenAlex

Abstract. Ocean alkalinity enhancement (OAE) seeks to increase the alkalinity of seawater for carbon dioxide removal (CDR). Following numerous propositions to trial, test, or upscale OAE for 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 does the following: (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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.368
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it