Untraceable: The Consequences of Canada's Poorly Regulated Seafood Supply Chains
Bibliographic record
Abstract
Seafood is one of the most highly traded food commodities in the world, with notoriously long and complex supply chains. In Canada, a lack of transparency in seafood supply chains is masking hidden costs – to the economy, our fisheries sector, ocean health and global human rights. Without adequate traceability in place, Canada is knowingly contributing to a global web of illegal, unreported and unregulated (IUU) fishing* and illicit seafood trade practices – those that involve money, goods or value gained from illegal and generally unethical activity. Our opaque supply chains are costing us: Canada is losing up to $93.8 million† in tax revenue each year due to the illicit seafood product trade, and Canadians are spending up to $160 million a year on seafood caught through IUU fishing. These practices also weaken the sustainability of fisheries and cheat both consumers and honest Canadian fishers. Right now, fish and seafood products caught by individuals who have been trafficked into modern slavery can make their way onto Canadian supermarket shelves, simply because safeguards to prevent this have not been put in place. The sale of these products makes Canada complicit in global human rights abuses and perpetuates the market for illegally caught fish.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".