Corruption and its Influence on Economy with Focus on Inflow of Foreign Direct Investment
Bibliographic record
Abstract
This paper evaluates theimpactof corruption oneconomic sphere, with special emphasis oninward foreign direct investment(FDI), as investment is oneof the main factorsof economic performance. The impact ofcorruptiononFDI inflowsisstudiedglobally.Based on the researchof contemporary literature, it was found that thelevel of corruptionhasnot clear negativeimpact onFDI, what is leadingto the formulationof the researchobjectives of this work.The results confirmthe88 countriesfor the years2000, 2005and 2011, the existence of anegative relationship betweenthe level of corruptionandFDI inflows, this is a statisticallysignificant relationshipand it isthis relationship furtherquantifiedwithin theregression model.In conclusion, it is proposed to include additionalexplanatoryvariablesin addition tothe degree of corruptionto understanding the causesFDI inflows.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".