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Record W7138351119 · doi:10.1080/02722011.2025.2557806

The Politics of Nuclear Waste Management and the Divergent Paths of the United States and Canada

2025· article· en· W7138351119 on OpenAlexaboutno aff
Barry G. Rabe

Bibliographic record

VenueThe American Review of Canadian Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsNuclear powerRadioactive wasteGovernment (linguistics)State (computer science)Waste disposal

Abstract

fetched live from OpenAlex

Nuclear energy expansion represents a rare area of energy, environmental, and climate policy with growing cross-partisan support in the United States and Canada in recent years. This reflects its capacity to combine non-carbon electricity addressing climate and air quality concerns with baseload power offering supply reliability. High-level nuclear waste remains an enduring challenge, however, as neither nation has established a permanent geological repository to provide secure management for tens of thousands of years. The United States has struggled for decades to address nuclear waste, including sustained intergovernmental battles over facilities proposed in three western states. In contrast, Canada has maintained a deliberative process that has made considerable progress in recent years in securing an Ontario host site through voluntary mechanisms, comparable to model cases from other nations. This suggests substantial policy divergence between the nations, one which might prepare Canada better than the United States for future nuclear energy expansion and export.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.128
Threshold uncertainty score0.931

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.011
Science and technology studies0.0090.015
Scholarly communication0.0090.002
Open science0.0010.002
Research integrity0.0010.002
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 head0.288
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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