Bibliographic record
Abstract
Since the implementation of NAFTA in 1994, extensive research has been conducted to analyze the effects of NAFTA on the U.S. economy. Most of this research has focused on aggregate gains and losses of the U.S. economy as a whole, but regional and statelevel assessments prove more accurate in describing specific impacts across the United States, as resources and industrial performance differ with even small movements in geographical location. This study seeks to analyze the effects of NAFTA on Kentucky's economy, as the state has been largely overlooked in terms of extensive evaluation. We used data obtained from WISERTrade, the World Institute for Strategic Economic Research, to begin a regression analysis specifying the effects of NAFTA on Kentucky exports. Total dollar values of exports to the top 25 countries that import Kentucky goods from 1988 to 2000, including Canada and Mexico, gave us a good idea of possible trends affected by NAFTA. We also looked at Gross Domestic Product and Real Gross Domestic Income for all of those countries for specified years so that we could determine any noticeable differences that could be attributable to the trade agreement. We expected to find that NAFTA has had a modestly positive effect on Kentucky's economy, specifically on export growth and diversity, a common finding for other regional and state-level assessments. We anticipated varied effects of NAFTA on the Gross Domestic Product and Real Gross Domestic Income of those countries labeled as primary recipients of Kentucky exports.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.002 |
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".