The Financial Setting for FDI Inflows into The Czech Republic and Slovakia
Bibliographic record
Abstract
This study examines the relationship between foreign direct investment in the Czech Republic and Slovakia and such potentially explanatory factors as trade flows, measures of economic and financial stability, and country risk. The authors find that as the Czech and Slovak Republics progress toward market economies, some policy points to consider include: transparency of markets, economic systems, social and political organizations; an increase in commerce and investment, which makes reversals of reforms less likely and the condition of financial factors that contribute to increased investment.At this time, these transition economies possess many of the resources needed for development, such as educated labor forces, an entrepreneurial orientation among the citizens, and available land. Yet, another key resource, capital, is in short supply in the region. An important precondition to obtaining capital is the demonstration of economic and political stability (see Kyrkilis and Pantelidi 2006). By improving the transparency of their legal, banking, and capital markets sectors, the Czech and Slovak Republics can accelerate their progress toward free markets and democratic societies.Our research finds that relationships exist among country risk ratings, financial market variables, and expected returns in this region; such relationships can be useful in developing policies to improve capital markets and attract external capital. The importance of building upon other researchers in this field updates that research plus incorporates new aspects of issues now developing. This research topic is fluid and demands continuous review and testing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".