Influencing Attitudes toward Carbon Capture and Sequestration: A Social Marketing Approach
Bibliographic record
Abstract
Carbon capture and sequestration (CCS), while controversial, is seen as promising because it will allow the United States to continue using its vast fossil fuel resources in a carbon-constrained world. The public is an important stakeholder in the national debate about whether or not the U.S. should include CCS as a significant part of its climate change strategy. Understanding how to effectively engage with the public about CCS has become important in recent years, as interest in the technology has intensified. We argue that engagement efforts should be focused on places where CCS will first be deployed, i.e., places with many "energy veteran" (EV) citizens. We also argue that, in addition to information on CCS, messages with emotional appeal may be necessary in order to engage the public. In this paper we take a citizen-guided social marketing approach toward understanding how to (positively or negatively) influence EV citizens' attitudes toward CCS. We develop open-ended interview protocols, and a "CCS campaign activity", for Wyoming residents from Gillette and Rock Springs. We conclude that our participants believed expert-informed CCS messages, embedded within an emotionally self-referent (ESR) framework that was relevant to Wyoming, to be more persuasive than the expert messages alone. The appeal to core values of Wyomingites played a significant role in the citizen-guided CCS messages.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".