Corruption and its Influence on Economy with Focus on Inflow of Foreign Direct Investment
Bibliographic record
Abstract
<p class="zhengwen"><span class="hps"><span lang="EN-US">This paper evaluates the</span></span><span class="hps"><span lang="EN-US">impact</span></span><span class="hps"><span lang="EN-US">of corruption on</span></span><span class="hps"><span lang="EN-US">economic sphere</span></span><span lang="EN-GB">, with special </span><span class="hps"><span lang="EN-US">emphasis on</span></span><span class="hps"><span lang="EN-US">inward foreign direct investment</span></span><span class="hps"><span lang="EN-US">(FDI</span></span><span lang="EN-GB">), as </span><span class="hps"><span lang="EN-US">investment is one</span></span><span class="hps"><span lang="EN-US">of the main factors</span></span><span class="hps"><span lang="EN-US">of economic performance</span></span><span lang="EN-GB">. </span><span class="hps"><span lang="EN-US">The impact of</span></span><span class="hps"><span lang="EN-US">corruption</span></span><span class="hps"><span lang="EN-US">on</span></span><span class="hps"><span lang="EN-US">FDI inflows</span></span><span class="hps"><span lang="EN-US">is</span></span><span class="hps"><span lang="EN-US">studied</span></span><span class="hps"><span lang="EN-US">globally</span></span><span lang="EN-GB">.</span><span class="hps"><span lang="EN-US">Based on the research</span></span><span class="hps"><span lang="EN-US">of contemporary literature</span></span><span lang="EN-GB">, it was found </span><span class="hps"><span lang="EN-US">that the</span></span><span class="hps"><span lang="EN-US">level of corruption</span></span><span class="hps"><span lang="EN-US">has</span></span><span class="hps"><span lang="EN-US">not clear negative</span></span><span class="hps"><span lang="EN-US">impact on</span></span><span class="hps"><span lang="EN-US">FDI</span></span><span lang="EN-GB">, </span><span class="hps"><span lang="EN-US">what is leading</span></span><span class="hps"><span lang="EN-US">to the formulation</span></span><span class="hps"><span lang="EN-US">of the research</span></span><span class="hps"><span lang="EN-US">objectives of this work</span></span><span lang="EN-GB">.</span><span class="hps"><span lang="EN-US">The results confirm</span></span><span class="hps"><span lang="EN-US">the</span></span><span class="hps"><span lang="EN-US">88 countries</span></span><span class="hps"><span lang="EN-US">for the years</span></span><span class="hps"><span lang="EN-US">2000, 2005</span></span><span class="hps"><span lang="EN-US">and 2011</span></span><span lang="EN-GB">, </span><span class="hps"><span lang="EN-US">the existence of a</span></span><span class="hps"><span lang="EN-US">negative relationship between</span></span><span class="hps"><span lang="EN-US">the level of corruption</span></span><span class="hps"><span lang="EN-US">and</span></span><span class="hps"><span lang="EN-US">FDI inflows</span></span><span lang="EN-GB">, this is a </span><span class="hps"><span lang="EN-US">statistically</span></span><span class="hps"><span lang="EN-US">significant relationship</span></span><span class="hps"><span lang="EN-US">and it is</span></span><span class="hps"><span lang="EN-US">this relationship further</span></span><span class="hps"><span lang="EN-US">quantified</span></span><span class="hps"><span lang="EN-US">within the</span></span><span class="hps"><span lang="EN-US">regression model.</span></span><span class="hps"><span lang="EN-US">In conclusion</span></span><span lang="EN-GB">, it is proposed </span><span class="hps"><span lang="EN-US">to include additional</span></span><span class="hps"><span lang="EN-US">explanatory</span></span><span class="hps"><span lang="EN-US">variables</span></span><span class="hps"><span lang="EN-US">in addition to</span></span><span class="hps"><span lang="EN-US">the degree of corruption</span></span><span class="hps"><span lang="EN-US">to understanding the causes</span></span><span class="hps"><span lang="EN-US">FDI inflows.</span></span></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".