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Record W2803951381

Governing Nuclear Waste : What Should Be the Roles of National Regulatory Bodies ?

2018· article· en· W2803951381 on OpenAlexaboutno aff
Céline Parotte

Bibliographic record

VenueORBi (University of Liège) · 2018
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
Fundersnot available
KeywordsRadioactive wasteBusinessEnvironmental planningWaste managementEnvironmental scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

In most nuclear countries across the world, geological disposal appears as the main option to manage high-level radioactive waste. Many new socio-economic, technical and safety issues arise with the implementation of this option. In the process of addressing these issues, national regulatory bodies stand out as crucial actors. While descriptions of their legal responsibilities abound, studies investigating the future normative choices emerging with the governance of toxic waste are needed. What should be the roles of regulators in dealing with the very sensitive siting process? According to whom? What does it mean for the credibility of the siting process? This presentation aims at systematically comparing three different regulation regimes and infrastructures of high-level radioactive waste management, based on actual perceptions of nuclear stakeholders of how, when and on what should national regulatory bodies intervene. Collected empirical data include a combination of empirical materials—i.e. legal requirements, safety case reports, participatory observations of consultation processes, 82 semi-directive interviews with policy makers, nuclear waste agencies, nuclear regulators in France, Belgium and Canada and local actors such as members of local information and monitoring council (CLIS) and members of the community liaison committee of four volunteer collectivities (CLC).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.273

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.198
Teacher spread0.179 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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