Canadians’ Opinions and Preferences regarding Seafood, and the Factors That Contribute to Their Consumption and Purchasing Habits
Bibliographic record
Abstract
Seafood provides rich sources of nutrients and generates relatively minimal environmental impacts; however, it tends to be underrepresented in discussions around food security. The objective of this study was to determine Canadians’ preferences and opinions regarding seafood consumption. Of the 1000 Canadians that were surveyed, most consume seafood regularly (87%). Apart from preference, nutrition was the primary reason for eating seafood (64%), and not affordability (21%). Younger Canadians, including Millennials (57%) and Gen Z (58%), placed a higher emphasis on preparation and cooking methods when purchasing seafood. Frozen seafood was the most popular selection for home consumption. Most Canadians prefer wild seafood; however, nearly as many had no preference for wild or farmed seafood. Most Canadians indicated that farmed seafood is a sustainable method of harvesting (49%), and many were willing to pay more for certified sustainable seafood. The environment and climate change are important factors when making food choices (54%), and most Canadians prefer to buy seafood that was harvested in Canada (74%). These results provide valuable insight into the attributes that Canadians value in their seafood choices. Sustainable, nutritious seafood with minimal environmental impacts should remain key areas of consideration to grow the seafood sector in Canada.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".