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Places and People: Rhetorical Constructions of “Community” in a Canadian Environmental Risk Assessment

2014· dataset· en· W4394360416 on OpenAlexaboutno aff
Philippa Spoel, Rebecca Carruthers Den Hoed

Bibliographic record

VenueFigshare · 2014
Typedataset
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRhetorical questionEnvironmental planningSociologyEnvironmental resource managementEnvironmental ethicsGeographyLinguisticsEnvironmental sciencePhilosophy

Abstract

fetched live from OpenAlex

This paper addresses the issue of public engagement in environmental risk contexts through a rhetorical analysis of the key term “community” in a risk assessment of mining-caused soil contamination. Drawing on Burke's concept of terministic screens and method of cluster criticism, the analysis shows the divergent constitutions of “community” in the Sudbury Soils Study's official discourse and the citizen-activist rhetoric of the Community Committee on the Sudbury Soils Study. Tracing the verbal and visual clusters within each organization's articulation of “community” as place and people reveals how the official Study's technical-regulatory ideology of environmental risk and citizen participation is countered by the Community Committee's contestatory environmental justice ideology. These competing views of “community” are mutually constitutive in that the official Study's mainstream risk discourse establishes the terms for the Community Committee's reactive counter-discourse, thus limiting citizen participation mainly to questions of “downstream” impacts. Our rhetorical analysis of “community” suggests a generative method for understanding the complex power relations animating specific risk communication contexts as well as for potentially reinventing “community” in terms more conducive to meaningful citizen engagement.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.556
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0790.000

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 head0.047
GPT teacher head0.270
Teacher spread0.223 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2014
Admission routes1
Has abstractyes

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