MétaCan
Menu
Back to cohort
Record W2011462120 · doi:10.2202/1944-4079.1080

A Systems View of Decision‐Making for Risky Technologies: From Global to Local and Local to Global

2011· article· en· W2011462120 on OpenAlexaffabout
Anneliese Poetz

Bibliographic record

VenueRisk Hazards & Crisis in Public Policy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNuclear powerStakeholderSituational ethicsSituation awarenessPublic relationsBusinessProcess (computing)Political scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract The recent disaster at Japan's Fukushima nuclear power generation facility has caused concern on a global scale over the safety of nuclear power. In response to heightened risk perceptions caused by these crises, many stakeholders may find processes for voicing their concerns to be inadequate. For many years, an effective model for decision‐making which includes public input has been needed, a process that meets the needs of diverse stakeholders in a democratic setting. Many studies conclude that stakeholder interactions occur within a complex system, but do not articulate details of this system. This study combined case study with grounded theory methodology and utilized situational analysis. Data were mainly comprised of 30 one‐on‐one interviews with stakeholders involved in the decision‐making processes for the re‐licensing of two nuclear power generation facilities in Ontario, Canada. The purpose of this paper is to present findings from this qualitative study which illuminate the complex interactions between global, national, and local systems, such as the effect of nuclear incidents on global and local risk perceptions. The paper concludes with several recommendations for organizations concerned with protecting public safety.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.823
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.039
GPT teacher head0.369
Teacher spread0.331 · 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 designOther design
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

Citations5
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueRisk Hazards & Crisis in Public PolicySame topicRisk Perception and ManagementFrench-language works237,207