Risk communication and public engagement in CCS projects: the foundations of public acceptability
Bibliographic record
Abstract
This paper posits that an important goal of public engagement for carbon capture and storage (CCS) projects as being public and social acceptance for those projects. It argues that acceptability is the end of a long, logical chain of social interactions, which ideally starts with: 1) the public perception of the risks and benefits associated with CCS; moves through 2) effective communication of risks and benefits by project proponents; 3) involves robust and credible measures for public engagement; 4) results in authoritative decision processes that transparently reflect the results of engagement. Each of these components of acceptability is described with respect to both actual experience with CCS projects to date and the relevant literature. Conclusions point to the special importance of full transparency and public understanding of credible risk assessments for these projects.
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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 | 0.040 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".