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Record W2046936827 · doi:10.1177/0162243915571166

Discourse Ecology and Knowledge Niches

2015· article· en· W2046936827 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScience Technology & Human Values · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of British ColumbiaUniversity of the Fraser Valley
FundersHealth CanadaCanadian Nuclear Safety Commission
KeywordsContext (archaeology)Meaning (existential)CertaintyGovernment (linguistics)The InternetPublic relationsEcological nichePerceptionRisk perceptionSociologyPolitical scienceEcologyEpistemologyGeographyBiologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

In this article, we investigate Internet discourses that capture Canadians’ perceptions of the risk of radiation from the 2011 Fukushima nuclear incident. We consider these online discourses of radiation risk in the context of recent Internet-based theories that explore ecological models of communication, and we take a discourse approach to our analysis of the online texts about Fukushima radiation risk. Our analysis reveals that, while government and scientific discourses about radiation risk are framed in terms of public concern and certainty, public discourses are framed in terms of uncertainty and gaps in public knowledge. Members of the public engaged in knowledge-seeking activities conducted their own nuclear risk assessments and disseminated the results to the interested public in street science activities. These public meaning-making activities, we argue, were generated by a desire to fill knowledge niches and attract public attention. They result in a discourse ecology characterized by epistemological rather than affective stances.

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.

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: Other
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 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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
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 score0.407
Threshold uncertainty score0.999

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 studies0.0020.016
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 head0.103
GPT teacher head0.354
Teacher spread0.252 · 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