Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As we write this, U.S. trade policy is falling into deeper and deeper disarray. The United States, Canada, and Mexico are holding frenzied meetings to renegotiate the North American Free Trade Agreement (NAFTA). As recently as October 11, 2017, President Donald Trump warned that he will withdraw the United States from NAFTA if he does not get a deal that is “fair” to American workers. Indeed, the Trump Administration has already pulled the United States out of the Trans-Pacific Partnership (TPP), threatened to withdraw from the United States-Korea Free Trade Agreement (KORUS), and is holding the World Trade Organization (WTO)’s vaunted dispute resolution process hostage to its demands for change. When modern U.S. trade policy was born in 1962, President Kennedy’s new trade-negotiation authority was explicitly linked to innovative domestic measures addressing harmed workers.[12] And during the original NAFTA negotiations, Mexico and the United States created and committed to funding the North American Development Bank to invest in projects along the Mexico-U.S. border—a precedent for coupling free trade agreements with international cooperation to ameliorate the costs of such agreements. It is time to build on that history and seize this opportunity to not only get NAFTA back on track, but put NAFTA at the forefront of addressing social inequalities through trade agreements. We begin in Part I by explaining the social contract of trade—a bargain whereby trade liberalization occurs in a way that ensures the least well off among us are, at a minimum, not harmed. Parts II and III explain how contemporary trade policy can reclaim that vision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it