THE NORTH AMERICAN FREE TRADE ASSOCIATION (NAFTA) AND ITS ECONOMIC EFFECTS
Bibliographic record
Abstract
The North American Free Trade Association (NAFTA) was founded in 1994 and includes the US, Canada and Mexico. Most of the tariff and non-tariff barriers were abolished after the agreement went into force. The Association has significantly contributed to deepening trade between member states and the growth of investments. NAFTA is an example of how the member states can benefit from free trade. Although NAFTA was established just 24 years ago, the benefits brought by it to the member countries are quite significant; in particular, gross domestic product (GDP) of the member countries increased, the GDP per capital increased, employment rate grew. NAFTA provides an example that free trade has and will have a future. According to various studies, since 1993 the number of the employed has grown by 40 million people. It should be noted that the U.S. export to Canada grew from 100 million USD in 1993 to 267 million USD in 2016 and to Mexico from 142 million USD to 498 million USD. The U.S. import from Canada amounted to 111 million USD in 1993 and 278 million USD in 2016; while import from Mexico grew from 151 million USD to 572 million USD. Every fourth job in Mexico depends on the export sector. The majority of economic analysis shows that NAFTA is beneficial for the economy of North America and the residents of the member countries; however, it damaged a number of sectors. In addition, it is argued that withdrawal from NAFTA and restoring trade barriers will negatively affect U.S. economy and expenses. However, Mexico will be much more affected as jobs will be reduced and economic growth will slow down both in short and long term perspectives. NAFTA has great support in the U.S. (as well as in Canada and Mexico). The survey by Gallup conducted in February 2017 showed that over 70% of Americans consider that NAFTA was good for America. Georgia has trade and economic (and not only) relations with NAFTA member countries, especially with its strategic partner US.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".