Bibliographic record
Abstract
After World War II and up until the 1980’s, the liberalization of trade was realized on a multilateral basis. World trade grew at twice the pace of GDP growth (Krueger, 1999). However, starting in the mid 1980’s, preferential trading arrangements (PTAs) increased in numbers. Perhaps the most influential PTA ever to be signed could be the North America Free Trade Agreement, or simply NAFTA, which came into effect January 1, 1994. The agreement established a free-trade area between its member countries- US, Canada and Mexico- in which all tariffs would be phased out between them, but each country would maintain its separate national barriers against the rest of the world. A lot of attention has been paid to the impact of NAFTA on the welfare of its member countries and on the rest of the world. This paper will focus on the impact of the agreement on the US’s beer trade flows by analyzing annual import and export data using several methods. To our knowledge there is no precedent for such research. Section II provides a brief review of the conclusions and methodology of existing works on NAFTA trade issues, as well as some important aspects of the agreement. Section III provides an overview of the world beer industry, and the NAFTA member countries beer markets. Section IV provides in great detail the methodology that we will employ. The focus of Section V is to explain the results obtained. Section VI provides conclusions and implications for further research on this subject. References and other sources can be found in Section VII.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".