Bibliographic record
Abstract
NAFTA(North America Free Trade Agreement)의 효과에 대한 많은 연구들이 실시되었다. 한국의 경우에는 한국과 미국의 FTA협상 이전에 이러한 연구들이 실시되었으나 실제 체결이 되고 난 이후에는 그러한 연구가 부족한 것이 현실이다. 한창 한국과 미국의 FTA가 이슈가 된 시기에는 데이터가 충분하지 못하였기 때문에 실증적인 분석에 한계가 있었으나, 현재는 충분히 데이터가 축적이 되었다고 볼 수 있어, 멕시코의 NAFTA 이후의 경제성장 이전과 이후를 비교 할 필요가 있다고 판단되어졌다. 분석결과로는 NAFTA 이후에는 해외직접투자가 경제성장에 부(-)의 영향을 주는 것으로 나타났다. 이는 후진국의 특성상 FDI가 산업에 생산에 효율적으로 활용되지 않는다는 것을 볼 수가 있다. 해외로부터의 송금은 정(+)의 영향을 주는 것으로 나타났다. 수출의 경우에는 경제성장에 부정적인 영향을 주는 것으로 나타났다. 본 연구는 몇 가지 한계를 가진다. 우선 산업을 분리하여서 분석을 실시해 볼 필요가 있다는 것이다. 또한 회원국인 캐나다와 미국의 분석을 같이 실시했으면 국가별로 NAFTA의 효과를 비교했다면, NAFTA의 전체적인 효과를 훨씬 잘 검정할 수 있을 것으로 예상할 수 있다.In the past, a number of studies have been conducted in favor of and against the effects of the North America Free Trade Agreement at a time when negotiations between South Korea and the U.S. will be discussed. However, at this point, it was deemed necessary to compare before and after Mexico s post-NAFTA economic growth. The analysis showed that Foreign Direct Investment has a negative impact on economic growth. This can be seen by the character of developing countries that FDI is not used efficiently in production in the industry. The remittances from abroad were found to have a positive effect. In the case of exports, economic growth is negatively affected. This study has several limitations. First of all, it is necessary to separate industries and conduct analysis. In addition, if the analysis of member countries Canada and the United States were analysed together, it would be expected that the overall effect of NAFTA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".