The Comprehensive Trade Agreement with India: What’s in It for Canada (Or India for That Matter)?
Bibliographic record
Abstract
Prime Minister Stephen Harper is leading a mission to India from November 3 to 9, 2012. On this, his second official visit to India, Harper will meet with the prime minister of India, Manmohan Singh, with the stated goal of strengthening trade and investment links between the two countries. In fact, the two countries have been negotiating a trade agreement known as the Comprehensive Economic Partnership Agreement (CEPA) since November 2010. The case for Canada to pursue a comprehensive trade agreement with India appears to be ironclad. India is a rapidly growing, very large economy with the second largest population and tenth largest GDP in the world. India is open for business and began economic reforms to liberalize trade and investment, deregulate industry and privatize state-owned enterprises in the early 1990s. India has been growing at an average rate of more than seven per cent per year since 1997. Moreover, India is a democracy and shares a colonial past with Canada. However, bilateral economic relations between the two countries have historically been very weak, but with the potential to grow substantially. It seems like India has all the right ingredients for Canada to make the CEPA a priority. So, what can Canada really expect from these negotiations? The answer: Unfortunately, not much.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".