Bibliographic record
Abstract
The Comprehensive Economic and Trade Agreement (CETA) is a trade deal between the EU and Canada and one of the most ambitious trade deals between the two blocs, Canada on one hand and the EU on the other hand. The combined effect of CETA on Canada’s gross domestic product (GDP) estimated to be about $7.9 billion which represents an average gain in income of about $220 per person ($2015). To enter Canada, tariff protection is considerable for many sectors which inhibit the imported products competing at Canadian marketplace. On dairy sector context, the products that are imported to be subject to excessive tariffs, occasionally over 300 per cent. For example, present out-of-quota tariffs concerning cheeses are 245.6%, which extensively supress the export of EU cheeses to Canada. With CETA, Canada agreed to establish two new tariff rate quotas (TRQs) for cheese originating in the European Union: One for 16,000,000 kg of cheeses of all types and another for 1,700,000 kg of cheeses of all types to be used in food processing. Howsoever, the industrial cheese quota will be made available entirely to further processors. In 2016, Canada’s cheese production increased from 386,937,000 kg in 2006 to 476,641,000 kg. Comparing the amount that has been allocated to the EU, it’s evident that around 3% of the Canadian production would enter into the domestic market. It may not seem like much, yet given Quota system of Canadian Dairy sector, CETA might create significant consequences in the quota-based production system.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.005 |
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".