Debate 2016: To TPP or Not TPP? Should the U.S. Join the Trans-Pacific Partnership and Other International Trade Agreements?
Bibliographic record
Abstract
Panel discussion given at Debate 2016 Symposium conducted at the Maurice A. Deane School of Law, Hempstead, New York. Transcribed by Haley Trust, Journal of International Business and Law. The TPP is an international trade agreement between twelve Pacific Rim nations including the United States. The other nations that'll join the TPP are Japan, Malaysia, Vietnam, Singapore, Brunei, Australia, New Zealand, Canada, Mexico, Chile, and Peru. Combined, the economies of these countries in the TPP have a combined population of eight hundred million people and comprise of about forty percent of the world's economy. Negotiations for the TPP have been going on in bits and parts, largely over the last seven years and have been a priority of the Obama administration for most of its two terms. Negotiations went on culminating in a final agreement signed on February fourth of this 2016. This panel discusses the pros and cons of the Trans-Pacific Partnership as well as many other such trade partnerships.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.027 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".