Bibliographic record
Abstract
Abstract Policy makers around the world have been advocating for an expansion of nuclear energy as a way to mitigate climate change, putting in place financial and political incentives for building new reactors and associated facilities. At the same time, policy makers have also been emphasizing the importance of incorporating justice considerations while decarbonizing. The two are not compatible because of the environmental injustices inflicted by the chain of processes required to generate electricity at nuclear power plants. These injustices are a result of the radioactive nature of the waste materials produced at each step of the nuclear fuel chain. Some of these materials remain hazardous for tens of thousands of years. In addition, nuclear facilities face the ever present risk of catastrophic accidents which can contaminate large tracts of land, rendering them uninhabitable for decades if not centuries. These consequences disproportionately fall on Indigenous Peoples and other disempowered communities, as well as non‐human entities. Such impacts are overlooked in our current socio‐political system committed to growth and a techno‐economic approach to dealing with any challenges to its continued existence. This article is categorized under: Human and Social Dimensions > Energy and Climate Justice Energy and Power Systems > Energy Infrastructure
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".