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Record W4386931111 · doi:10.1002/wene.498

Nuclear power and environmental injustice

2023· article· en· W4386931111 on OpenAlexaff
Johanna Höffken, M. V. Ramana

Bibliographic record

VenueWiley Interdisciplinary Reviews Energy and Environment · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNuclear powerInjusticeEnvironmental justiceIncentivePoliticsBusinessRadioactive wasteIndigenousHazardous wasteNatural resource economicsPolitical scienceEnvironmental planningEnvironmental resource managementEngineeringEconomicsLawGeographyWaste managementMarket economy

Abstract

fetched live from OpenAlex

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 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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.293
Teacher spread0.272 · 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
GenreEmpirical

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

Citations22
Published2023
Admission routes1
Has abstractyes

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