Places and People: Rhetorical Constructions of “Community” in a Canadian Environmental Risk Assessment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.026 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".