Literature Review in Field of Factors Influencing the Attraction of FDI and Its Spillover Effects
Bibliographic record
Abstract
The study of FDI and its identification as a specific type of investment activity began in the `60s of the XX century, when under the influence of the beginning of the process of globalization, transnational cooperation began to form, which actively entered the national markets of the countries of the world using FDI. The current study reviews the literature in the field of factors influencing the attraction of FDI. This goal has necessitated the formulation of the following tasks: to investigate the basic theoretical and empirical research; determine the directions of the influence of FDI on the economy of the state; literature research on the side effects of FDI. The main research methods are bibliographic analysis, critical analysis of scientific approaches solving FDI problems, retrospective analysis of literary sources. Most of the scientific literature focuses on the study of individual factors in attracting FDI to the economy of host countries. Simultaneously, the scientific discussion continues among foreign and domestic scientists regarding the formation of an optimal set of factors to stimulate the growth of the inflow of FDI in the economy of the recipient country. According to the results of the study, the author noted the presence of many problematic issues that require further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".