Bibliographic record
Abstract
As we write, the United States, Canada, and Mexico are meeting in Washington, D.C. to renegotiate the North American Free Trade Agreement (NAFTA). These talks—and their possible failure—represent the biggest shift in U.S. economic policy in a generation. Since NAFTA came into force in 1994, it has transformed the North American economy. NAFTA has made possible continent-wide supply chains, in industries like the auto sector, that have reduced costs and allowed American automakers to remain competitive; it has opened markets for American agriculture; it has greatly increased the standard of living in Mexico; and it has reduced consumer prices across the continent. Despite these gains, President Trump has repeatedly threatened to pull the United States out of NAFTA if he cannot get a deal that is “fair” for American workers. These repeated threats, coupled with aggressive U.S. government proposals to roll back liberalization in NAFTA 2.0, have sent the Canadian and Mexican governments and the U.S. business community searching for new policy ideas to save free trade.\nRestoring Trade’s Social Contract answers this call by proposing a financial transaction tax (FTT) in NAFTA and future trade agreements. The tax, no more than .1% of the value of securities or currency sales within the free trade area, would raise revenue to fund an expansion of adjustment assistance for workers who are displaced due to trade liberalization. An Economic Development Chapter in NAFTA and future trade agreements would mandate that this revenue be spent on expanded domestic trade adjustment assistance programs, such retraining, relocation assistance, and infrastructure investment.\nOur proposed tax would thus directly harness the wealth-creating potential of trade agreements and explicitly tie funding for adjustment assistance to major financial institutions, the parties benefitting the most from trade agreements. In so doing, it would restore what we term the social contract of trade—a bargain whereby trade liberalization occurs in a way that ensures that the least well off among us are, at a minimum, not harmed. Despite its huge contributions to poverty reduction and increased human welfare since World War II, trade liberalization has contributed to significant job losses, leading to economic calamity and social disruption in industrial heartlands from the mid-Western United States to Manchester, England and Wallonia, Belgium. These economic losses, in turn, have spurred the political backlash that now threatens the international economic order. Securing the long-term benefits of trade liberalization for ourselves and our fellow citizens—making free trade politically sustainable—thus requires including in trade law itself measures to address these significant costs. With NAFTA talks ongoing and the United States debating tax reform, the time is right for an FTT dedicated to expanded adjustment assistance.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".