The determinants of inwards Foreign Direct Investment (FDI) in Malaysian investment activities
Bibliographic record
Abstract
The aim of this study is to examine the determinants of Inwards Foreign Direct Investment (FDI) in Malaysian Investment Activities. The determinants are selected from macroeconomic factors that are considered to influence the inward FDI based on previous literature. The selected determinants are Size of Domestic Market, Exchange Rate, Inflation Rate and Interest Rate. Secondary data is collected for the variables ranging from the first quarter (QI) 2005 to fourth quarter (Q4) 2015. Based on the Multiple Linear Regression Model used in the study, Size of Domestic Market proxy by the Gross Domestic Product is the main determinant that affects Inwards FDI in Malaysia due to its significant positive influence. It is believe that an increase in GDP will increase the FDI inflows into Malaysia investment. On the other hand, the remaining variables have stumbled accross insignificancy and is unable to explain the relationship towards Inwards FDI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".