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Record W4401917482 · doi:10.4324/9781003422686

Intergenerational Democracy, Environmental Justice and the Case of Nuclear Waste

2024· book· en· W4401917482 on OpenAlexaboutno aff
Lee Towers, Matthew Cotton

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental justiceDemocracyEconomic JusticePolitical scienceEnvironmental ethicsEnvironmental planningSociologyEnvironmental scienceLawPhilosophyPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.815
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.015
GPT teacher head0.274
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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