Consumption Advisories and Compliance: The Fishing Public and the Deamplification of Risk
Bibliographic record
Abstract
Managers and regulators have recognized that the fishing public often ignores fish consumption advisories, and the reasons for non-compliance are explored in this paper. Risk assessors acknowledge that there is a social amplification (intensification) of risk where the public perceive a risk as much more severe than do the 'experts' or scientists, and this social amplification is a function of the interaction of hazards with social, psychological and cultural processes. I propose that non-compliance of consumption advisories occurs because of the deamplification of risk in hazards that are familiar and enjoyed, such as fishing and fish consumption. Although the public are generally aware of consumption advisories, they continue to believe the fish are safe to eat, and a high percentage eat the fish they catch. Unlike the amplification of risk, the deamplification of risk from fishing in the face of consumption advisories is partly legitimized by the actions of some governmental agencies, as well as by society at large. It is suggested that a variety of economic benefits and social institutions lead to a discounting of consumption advisories, and the delayed nature of adverse health effects allows for additional disregard. Further, it is suggested that co-management of the risk from contaminated fish would increase public involvement, and therefore compliance.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".