Integrated Model to Measure the Impact of Terrorism and Political Stability on FDI Inflows: Empirical Study of Pakistan
Bibliographic record
Abstract
To condense saving-investment gap, transformation of technology, creation of employment opportunities and more importantly, increasing economic development of host countries, Foreign Direct Investment (FDI) is proven to be a significant source of investment predominantly for developing countries. Numerous standing studies have scrutinized the economic impact of terrorism and political stability by referring to decrease in FDI. This study empirically enlightens the determinants of FDI for Pakistan over the period 1970 to 2013, by using annual secondary time series data. Adopting the optimistic approach, in this study, variables in the combination of terrorism, political stability, trade openness and GDP have been analyzed applying Ordinary Least Square (OLS) method. As expected, the projected results confirm that GDP, trade openness and political stability have positive and significant impact whilst terrorism has negative influence on FDI inflows in Pakistan. Because of the political stability along with stable GDP growth rate, inverse impact of terrorism has been found statistically insignificant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".