Where Science Meets Resistance: Transforming Geoscientists into Effective Communicators
Bibliographic record
Abstract
Abstract Carbon Capture and Storage (CCS) faces challenges when proponents are communicating difficult concepts and science to members of the general public, to the media, or to government officials. Turning scientific concepts into public acceptance and license to operate is particularly challenging for CCS, when doubts about climate change, conspiracy theories, and combative political discourse overpower science and project planning. Effective communications planning can anticipate and counteract these forces leading to successful project implementation. The Aquistore CO2 Deep Saline Storage Project, in southeastern Saskatchewan, Canada, has developed effective communications and outreach strategies that have become important bellwethers for other projects thinking of moving forward with CCS. The project developed both a local public outreach strategy, along with print and video materials that have been successful in soliciting broad public support for the project over the past 15 years. But as social media has developed over the life of the project, addressing new challenges arising from dis- and misinformation has proven more difficult and has required more vigilance and methods of counteracting such sources. Aquistore has provided the opportunity to develop, over time, new methods of information and project dissemination that have been useful learning tools for the geoscientists and engineers involved in the project to better communicate the importance of CCS to audiences that are becoming more and more fractured and influenced by disparate and misleading media sources. This has meant proponents have had to increase the project’s social media presence, along with attending more events and conferences outside of science and geophysics to spread accurate information. The project has also seen its staff and scientists become more vigilant in observing the ways artificial intelligence is being trained to speak of CCS in both negative and positive ways. AI’s relationship to CCS has, in some ways, become a battleground for competing and often opposing political views about climate change, making it even more important for CCS scientists to develop ways of explaining CCS that are clear to broader audiences. Aquistore has developed effective outreach and communications strategies that have developed in innovative ways over the project’s 15 years that address areas like AI and disinformation that have rarely been faced by such projects in the past, and which have required geoscience professionals to learn new ways of providing facts and information.
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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 | 0.055 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.025 | 0.054 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".