Intergenerational Democracy, Environmental Justice and the Case of Nuclear Waste
Bibliographic record
Abstract
This book explores the interplay between intergenerational justice and intragenerational justice using nuclear waste management as a consistent case to explore these themes. Lee Towers and Matthew Cotton examine the issue of intergenerational justice from a social scientific perspective, drawing on central case studies of nuclear waste management in Canada, Finland, and the United Kingdom. They connect indigenous philosophies and notions of justice with the concept of intergenerational democracy, advocating for better inclusion of youth and elders in decision-making that affects their well-being. As such, the book’s primary objectives are fourfold: To assess whether trade-offs between intergenerational and intragenerational justice are necessary, and if so, what these trade-offs are and how they might be resolved.To critically assess dominant western liberal philosophical approaches that shape contemporary intergenerational justice thinking in policy and practice, and consider alternatives drawn from anthropology and indigenous philosophies.To assess how far our current capitalist system can achieve substantive forms of justice.To critically examine three nuclear waste management case studies and assess how far these achieve environmental and energy justice and how they exemplify tensions between inter- and intragenerational justice. This short, accessible volume will be of great interest to students and scholars of energy, environmental justice, and ethics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".