Qatar Emerging as the Most Attractive FDI Destination in the GCC
Bibliographic record
Abstract
In this era of globalization and high competition each and every nation is trying to attract maximum investments from overseas for the development and growth of the economy. All GCC economies are in the growth mode and are dependent on the revenue from oil, but the current decline in oil prices have very badly affected these economies and all are targeting to secure maximum foreign direct investments (FDI). Many of the developed nations and Multinational Corporations are in the search of the best destinations for their investments. The purpose of this study is to identify the most attractive destination for FDI in the GCC. The flow of FDI into a country depends on the availability of a number of factors. This study probes into the existence of each of these factors in the various GCC countries. Secondary data is used to rank each country on the basis of the parameters that attract FDI. The findings indicate that Qatar is emerging as the most attractive FDI destination in the GCC. This paper is useful for all countries and MNCs who are searching for investment destinations in the GCC as it ranks the countries on the basis of the attractiveness of various determinants of FDI.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".