(Mis)managing a risk controversy: the Canadian salmon aquaculture industry’s responses to organized and local opposition
Bibliographic record
Abstract
In the past few years, salmon aquaculture has become one of Canada’s most controversial industries. Environmentalist and other oppositional groups have mounted aggressive communications campaigns on issues such as the environmental and health impacts of the industry. In coastal regions, local opinion is divided, with some stakeholders and First Nations (indigenous) groups vehemently opposing the industry, while others see it as an important contributor to stressed coastal economies. In this article, we analyse industry responses to both organized and local opposition. Existing research on risk communication and ‘risk issue management’ tells us that important strategies for addressing controversy include building public trust, acknowledging the legitimacy of critics and their concerns, engaging in transparent and pro‐active risk communication, establishing meaningful partnerships with stakeholders, and ultimately reforming controversial practices. Drawing on an analysis of advocacy materials and transcripts from public hearings into aquaculture, we conclude that the salmon aquaculture industry has been largely unsuccessful in its attempts to blunt criticism from organized oppositional groups, but has taken some important (if tentative) actions to enhance its legitimacy at the local level.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.044 | 0.016 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".