How Differing Trade Policies May Impact U.S. Agriculture: The Potential Economic Impacts of TPP, USMCA, and NAFTA
Bibliographic record
Abstract
In the last two years, the United States has reversed the post-World War II trend toward the lowering of trade barriers and a commitment towards multilateral free trade. Citing a need to “level the playing field” and hold trading partners accountable to their commitments, the current Administration has moved towards a more protectionist and perhaps mercantilist position vis-à-vis trade policy. One of the Administration’s first actions in this regard was the decision to leave the Trans-Pacific Partnership (TPP) agreement, followed thereafter by raising tariffs on steel and aluminum imports. The Administration’s actions on trade are likely to have significant implications for U.S. farmers as these actions target three of the largest markets for U.S. agricultural exports – Canada, China and Mexico – accounting for some 44%, and representing an average of $63 billion, of U.S. agricultural exports 2013 to 2015. <em>Commissioned by the <a href="https://www.farmfoundation.org/">Farm Foundation</a></em> <strong><a href="https://www.farmfoundation.org/forums/2019-farm-foundation-forums/u-s-and-canadian-perspectives-on-trans-pacific-trade/">Farm Foundation Forum</a></strong> (March 4, 2019) <ul> <li><a href="https://soundcloud.com/user-254829763/us-canadian-perspectives-on-trans-pacific-trade">Forum audio</a></li> <li><a href="https://www.farmfoundation.org/trade/">Food and Agricultural Trade Resource Center</a></li> </ul> <strong><a href="https://www.farmfoundation.org/forums/2018-farm-foundation-forums/oct-31-2018-farm-foundation-forum/">Farm Foundation Forum</a></strong> (October 31, 2018) <ul> <li><a href="https://brianallmerradionetwork.wordpress.com/2018/10/31/10-31-18-a-closer-look-at-the-purdue-universitys-global-trade-analysis-project-regarding-usmca-with-purdue-ag-economist-dominique-y-van-der-mensbrugghe-ph-d/">van der Mensbrugghe Interview</a></li> </ul>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".