Part of the Problem or Part of the Solution? MNEs, FDI, and the Cycle of Corruption in Africa
Bibliographic record
Abstract
Corruption is a persistent and pervasive concern worldwide, leading to lower levels of economic growth, the erosion of political and legal institutions, and decreased foreign and domestic investment. Yet, many questions remain about the role that multinational enterprises (MNEs) play in exacerbating or mitigating corruption. We address such questions by examining corruption in sub-Saharan Africa, a context with increasing levels of economic growth and foreign investment, but varying levels of success in addressing corruption. We generate new insights with respect to the nature of corruption itself, causes of corruption, consequences of corruption, and how corruption can be fought. We find that rather than corruption being an inherently unavoidable ‘cost of doing business’ in developing countries, the attributes of FDI and actions of MNEs play a significant role in influencing whether corrupt activities are enabled or curbed in a host country.
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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 | 0.002 | 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 | 0.001 |
| 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".