Bibliographic record
Abstract
Since the North American Free Trade Agreement (NAFTA) entered into force in 1994, production within the three NAFTA countries has become more specialized as foreign direct investment and trade have been allowed to thrive and firms have taken advantage of economies of scale and lower wages in Mexico. Extensive regional supply chains for producing motor vehicles, chemicals, wearing apparel, among other commodities have emerged. Using a global trade model tailored to include supply chains, we examine the impact of the United States extricating itself from the NAFTA. US tariffs on imports of goods from Canada and Mexico, currently covered under the NAFTA, are assumed to rise to US most favored nation (MFN) rates, compelling Canada and Mexico to reciprocate under World Trade Organization (WTO) rules. Overall, the results show that the United States’ reversal of NAFTA leads to a decline in real Gross Domestic Product (GDP), trade, and investment in the United States, Canada, and Mexico, with most of the losses resulting from Canada and Mexico’s reciprocation. The losses in low-skilled employment are most significant, with employment declining by 256, 000, 125, 000, and 951, 000 in the United States, Canada, and Mexico, respectively. Production and specialization of production across the NAFTA region declines, particularly in those sectors with the highest levels of vertical specialization across NAFTA. The motor vehicles and services sectors in all three NAFTA countries decline, along with production of US meat, food, and textiles; Canadian chemicals and metals; and Mexican textiles, wearing apparel, electronics, and machinery.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".