The (un)sustainable game: An exploration of rhetorical strategies and risk communication in sustainable seafood campaigns
Bibliographic record
Abstract
In the 1990s, following the Newfoundland Grand Banks cod fishery collapse along Canada’s East Coast, the first seafood sustainability certification organization was formed to address this widespread crisis. Two notable campaigns were formed shortly thereafter, both programs the projects of marine aquariums along the West Coast, and have gained significant attention: Vancouver Aquarium’s Oceanwise provides seafood recommendations to restaurants on the most sustainable choices and Monterey Bay Aquarium’s Seafood Watch, creates and disseminates consumer guides. This MRP examines the communication strategies of Seafood Watch and Ocean Wise used to encourage the consumption of sustainable seafood and promote ocean conservation. More specifically, this MRP analyzes the organizations’ use of environmental rhetoric, particularly in terms of framing and topoi, and how they communicate risk and urgency. How sustainable seafood campaigns establish credibility and rationale in the public sphere to communicate urgent, technical information surrounding fishery mismanagement is examined. This research will help inform future guidelines for social marketing campaigns to improve strategy and encourage consumer change. Recommendations for future research include the creation of evaluative programs to measure campaign effectiveness as well as an analysis of the niche markets established through the rising sustainable seafood market.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".