(Mis)managing a risk controversy: the Canadian salmon aquaculture industry’s responses to organized and local opposition
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.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it