The Social Construction of Political Risk: How Narratives Shape Perception and Constrain investments
Bibliographic record
Abstract
Systemic underinvestment in Africa is often attributed to a deficient institutional environment ostensibly due to the political risk caused by corrupt governments, crime, and civil wars. However, leading intergovernmental organizations have recently noted that these perceptions of the political risk in Africa are increasingly misaligned with the brighter macroeconomic reality of the continent. In this paper, we build theory on the “risk perception premium” that is hindering investment and economic development in Africa. Drawing on rhetorical history, we link nostalgic and dystoric narratives about Africa to perceptions of institutional quality and postalgic and dystopian narratives to perceptions of institutional change. These factors influence how opportunities to invest in Africa are evaluated (in terms of feasibility and possibility) and ultimately actual investment on the continent. Our theory of socially constructed political risk builds a subjectivist counter perspective to the dominant rationalist approach to foreign investment decisions and offers important insight into the pernicious challenge of underinvestment in African economic 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".