Foreign Direct Investment (FDI) by Asian and European Union (EU) Countries: The Investment Effects of Pharmaceutical Sector
Bibliographic record
Abstract
The importance of Foreign Direct Investment (FDI) in current era is clearly seen in the investment made by carious companies abroad and also receiving international investment by local companies. The countries are involved in outwards and inwards FDI to receive maximum profit and long-term benefits from various FDI projects. In this regard, outwards foreign direct investment (OFDI) is also getting increased importance along with inwards FDI. The current study thereby intends to investigate the OFDI trends of companies from European Union and Asian countries whereby focusing on pharmaceutical sector particularly. Moreover, it is noteworthy that there is a rapid growth in pharmaceutical industries around the world due to advanced technology and infinite amassed need for treatment and cure options. Therefore, current study targets three European Union countries and three Asian countries, including both developed and developing nations, and investigates the OFDI patterns of pharmaceutical sector of countries. The results reveal that EU states are effective in both attracting FDI and making investments abroad and in domestic markkets; however, Netherland among three states has the highest performance in FDI inflows and outflows patterns. The analysis of Asian countries indicated that they are less effective in terms of their FDI flows compared to EU states. This is because of their developing stage and less economic growth stages. However, among three countries, Turkey presents the highest performance in attracting FDI from international markets, while Israel has highest performance in making investments abroad among three countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".