Local and ocean-friendly: An overview of the sustainable seafood movement in Vancouver
Bibliographic record
Abstract
An example of successful public engagement will be presented in this session through a panel discussion that includes experts from non-governmental organizations and the restaurant industry. Vancouver was recently named one of the Top 10 Foodie Tourism Destinations in the World Lastly by Travelocity (2014), specifically for its substantial commitment to sustainable seafood. Following an overview of some success stories of the Vancouver Aquarium’s Ocean Wise program to date (namely within Vancouver and surrounding area), panel participants will discuss the ways in which their businesses or organizations have successfully engaged the community to raise awareness of sustainable seafood, while also promoting the consumption of local fish and shellfish species from the BC coast. As well, in keeping with the overarching conference theme of Strengthening Connections in Changing Times, a specific emphasis will be placed on the importance of collaboration between these different groups (NGOs, fishing industry, chef community) and how this connection ensures cohesion in messaging and builds trust with seafood consumers. Participants will also discuss some of the biggest challenges and limitations of promoting the message of sustainable seafood to a broader audience (e.g., Canada-wide, different ethnic groups) and the future direction of the Ocean Wise program.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".