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Record W4285394380 · doi:10.1177/01622439221112459

The Efficacy Paradox Revisited: “Closing Up” Commitments in Nuclear Waste Governance

2022· article· en· W4285394380 on OpenAlexaboutno aff
Céline Parotte, Hadrien Macq, Pierre Delvenne

Bibliographic record

VenueScience Technology & Human Values · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
FundersHarvard Kennedy SchoolFonds De La Recherche Scientifique - FNRSUniversitat Pompeu Fabra
KeywordsClosing (real estate)Corporate governanceNuclear powerProcess (computing)Citizen journalismVariety (cybernetics)Closure (psychology)Political scienceBusinessPublic relationsEconomicsKnowledge managementComputer scienceManagementLaw

Abstract

fetched live from OpenAlex

It is well established in science and technology studies that participation and expert analysis should not be seen as contradictory. Key analytical questions include how both public and expert knowledge contribute to “closing down” and “opening up” appraisals and commitments, and how important these dynamics are in assessing the process and the conditions of democratizing technology. This article examines how the participatory turn has affected nuclear waste governance options in France and Canada. Through cross-case analysis, it describes how at each constitutive step of management programs, public and expert knowledge has followed a variety of pathways in (in)forming commitments, resulting in asymmetrical trade-offs. The term “closing up commitment” is introduced to refer to the way both national governments finally opted for closing the technological options at hand while introducing new conditions that might challenge future actions. We argue that paying attention to this mutation in nuclear governance allows for a more detailed analysis of power distributions in science and technology governance than a critical approach that rejects any closure because it can be (and often is) the result of an instrumental approach undertaken by the incumbent actors.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptScience and technology studies
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.035
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.052
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0070.054
Scholarly communication0.0110.020
Open science0.0030.013
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0060.000

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 head0.027
GPT teacher head0.346
Teacher spread0.319 · 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

Labeled directly by 2 models reading the full record.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical · Commentary

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

Citations4
Published2022
Admission routes1
Has abstractyes

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