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Record W4388561012 · doi:10.1016/j.jenvrad.2023.107311

People do not have high levels of knowledge of low dose ionizing radiation (LDIR)

2023· article· en· W4388561012 on OpenAlexafffundabout
Margot Hurlbert, José Cóndor, Dazawray Landrie-Parker, Larissa Shasko

Bibliographic record

VenueJournal of Environmental Radioactivity · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of SaskatchewanYukon UniversityUniversity of Regina
FundersCANDU Owners Group
KeywordsHarmNon-ionizing radiationPsychologyPublic relationsPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Through survey and focus groups in two provinces in Canada misunderstanding and confusion surrounding Low-Dose Ionizing Radiation (LDIR) is explored specifically surrounding medical procedures, risk, and benefits. Generally people associated the word radiation with harm, but when asked participants were not concerned about LDIR. Approximately equal numbers (40%) thought LDIR was 'difficult' as those that thought it was 'easy' but research results reveal confusion about the definition of and sources of LDIR. Most people believed the benefits of LDIR outweighed the risks. Further, many had inaccurate views of 'high' dose radiation. Scientists and the Canadian regulator were determined to be the most trusted sources of information while elected officials and industry representatives the least trusted. Participants wanted more information on whether LDIR was a problem in Canada, what the risks were associated with it, as well as the applicable protections, rules and regulations. Focus group participants expressed a preference for face-to-face exchange of information, but mass media remains an important source of information as the first-place people check for answers. Future research surrounding behavioural science and LDIR communications, and deep LDIR science communication will be important in addressing this issue.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.730

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.300
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 teacher head, not a consensus.

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

Citations7
Published2023
Admission routes3
Has abstractyes

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