MétaCan
Menu
Back to cohort

Place, Culture, and the Social Amplification of Risk

2006· article· en· W1996204046 on OpenAlexafffundabout
Jeffrey R. Masuda, Theresa Garvin

Bibliographic record

VenueRisk Analysis · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsUniversity of AlbertaAlberta Environment and Protected AreasMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSituatedRisk communicationPublic relationsEnvironmental planningBusinessPolitical scienceSociologyGeography

Abstract

fetched live from OpenAlex

This article investigates the role of culture in the social production of risks and risk communication surrounding industrial development in a region located at a rural-urban interface. A case study examined a public consultation that was undertaken to inform local residents about an eco-industrial development proposal being planned near Edmonton, Alberta, Canada. The research employed the social amplification of risk framework (SARF) to examine the relationships among culture, place, and socially constructed risk. A total of 44 in-depth, semi-structured interviews were carried out with 33 landowners (farmers, acreage owners), public officials (municipal politicians, administrators), journalists, and industry representatives. Analysis revealed that risk communication occurred in relation to situated experiences of place that were based on conflicting cultural worldviews. The research shows that place is a useful component of the SARF, providing a spatial explanation for why some people amplify, and others attenuate, risks in locally contentious environmental debates.

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)0.003
metaresearch head score (Gemma)0.005
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.022
Scholarly communication0.0060.003
Open science0.0010.006
Research integrity0.0010.001
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 head0.008
GPT teacher head0.293
Teacher spread0.285 · 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

Citations166
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueRisk AnalysisSame topicRisk Perception and ManagementFrench-language works237,207