Solar geoengineering: Scenarios of future governance challenges
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.
Bibliographic record
Abstract
In the face of increasingly clear climate-change impacts and continued inadequacy of efforts to reduce greenhouse-gas emissions and adapt to ongoing climate changes, increasing attention has been directed to geoengineering: deliberate large-scale interventions in the Earth’s climate system to moderate global warming. Such interventions could reduce risks in novel ways, but are controversial because they present an uncertain, high-stakes mix of potential benefits and risks. Solar geoengineering poses especially acute international governance needs, particularly in the case of potential future demands to use it. Many aspects of geoengineering present deep, ill-structured uncertainties that carry high stakes for near-term decisions, and are thus suitable for exploration through scenarios. This collection of papers reports on a major scenario exercise examining governance challenges and potential responses for solar geoengineering, held at the International Summer School on Geoengineering Governance in Banff, Canada in 2019. This opening paper introduces geoengineering and the concerns it raises, particularly as they pertain to governance; reviews the design and use of scenario exercises to inform decisions under uncertainty, including their prior uses related to climate change and geoengineering; and outlines the aims, design, and process of this scenario exercise.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it