Fortress America As Created By NAFTA And Its Impact On Foreign Direct Investments
Bibliographic record
Abstract
The inauguration of the North American Free Trade Agreement (NAFTA) by the United States, Canada, and Mexico in 1994 created an outstanding economic opportunity for all three members. It contains an ambitious schedule for elimination of all trade barriers in the NAFTA community. The free trade agreement encourages NAFTA members to share and complement each others resources, and to use comparative advantages and enhanced economies of scale to maximize efficiency and productivity. Accordingly, the insider MNCs can generate competitive advantage against outsider MNCs in their trading bloc and globally.If NAFTA could create such a positive economic impact for insider MNCs and negative economic impact for outsiders, how do foreign MNCs respond to this trading bloc? Particularly, how does the fortress America, as created by NAFTA, impact the foreign direct investment strategies of outsider MNCs?The analysis of investment patterns by foreign MNCs since implementation of the NAFTA indicates an increase in their foreign direct investments in the NAFTA community, particularly in Mexico. This increase in FDI by foreign MNCs could be explained in three ways: First, it is due to the fear of fortress America as MNCs invested in the NAFTA bloc to penetrate the regions open market. Second, investors are looking to capitalize on the tangible financial attractions of modern-day Mexico, namely its location as a Latin America gateway, affordable primary resources, and increasingly competitive open market conditions. Third, the MNCs who had capital and global plans for such intensive foreign investments had already permeated North America.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".