Attracting Chinese Foreign Direct Investment (FDI) to Africa: Determinants and Policies - The Case of Guinea
Bibliographic record
Abstract
This study examines the determinants and policies for attracting Chinese FDI to Africa by specifically analyzing major characteristics, trends and developments in the economic engagement between Guinea and China. Guinea’s selection as the case study is justified by the country's experience of macroeconomic instability and social policy since independence. By considering mechanisms that play important roles in attracting FDI; market size, economic growth, employment, degree of trade openness and trade policy of the recipient country, the results show positive R-squared- a valid regression. However, all of these coefficients of determination are still not significant enough. The disparity to attract investment is assessed from geographical location, infrastructure, corruption levels, and income yields to implementation of the policies by the governments. It recommends policies at both national and bilateral levels in order to increase large Chinese FDI inflows towards Guinea and improve the forecast for macroeconomic and its constant development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".