The Role and Nature of Export Credit Agencies in Foreign Direct Investment: Home and Host States' Coordination and the Problem of Political and Commercial Risks Distinguished
Bibliographic record
Abstract
Since political instability and the fear of expropriation in some developing states pose grave risks to foreign investors, along with the behavior of the financial markets of these states, the need to insure against such risks is something to be considered in order to encourage investments in these regions. ECAs have also been established to assist foreign investors conducting their business in a given market through granting them loans, guarantees and insurance against certain risks encountered by investors. These loans to foreign investors are granted in order to ease their entry into the foreign market so that the recipient market benefits from the expertise and technology that the foreign investor possess. \nThe study will start with introducing a comprehensive definition for investment in light of the suggested view to what foreign investment stands for. It will highlight the difference between direct and indirect investments as well. Then it will move on to discuss entry of investors to foreign markets and it will discuss the open-door and closed-door approaches in order to identify the various risks associated with such investments. The thesis will emphasize on the need to have a more detailed approach towards investment-risks based on the five-risk distinction rather the classic three-risk approach. This will be of importance especially since the borderline \nbetween these risks interlink at times.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".