Bibliographic record
Abstract
Nuclear energy technologies have the potential to help mitigate climate changeClimate change. However, these technologies face many challenges, including high costs, societal concern and opposition, and health, safety, environmental and proliferation risks. Many companies and academic research groups are pursuing advanced designs, both fission and fusion-based, to address both costs and these risks. This chapter complements these efforts by analyzing how nuclear technologies can address societal concerns through the acquisition of a social licenseSocial license, a nebulous concept that represents ‘society’s consent’ and that has been used to facilitate and improve a wide range of publically and privately funded projects and activities subject to a range of regulatory oversight, including large industrial facilities, controversial genetic engineering research, and environmental management. Suggestions for public engagement and consent-based siting, two aspects of a social license, have been made before. The chapter modernizes these suggestions by briefly reviewing the social license and engagement literature. It discusses, in the context of how to acquire a social license, the role of government regulation, the role of project proponents and government actors, and the role of four key principles, including engendering trust, transparency, meaningful public engagement, and protection of health, safety and the environment. Further, the chapter uses the social licenseSocial license concept to explain why some nuclear waste repositoriesNuclear waste reositories have succeeded while others languish, and it provides concrete recommendations for the deployment of new nuclear waste repositories and advanced power plants, both fission and fusion-based.
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.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".