Stuck in a Fragility Trap: The Case of the Central African Republic Civil War
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study utilizes the synthetic control method to assess the economic consequences of the ongoing civil war in the Central African Republic since December 2012. Drawing on a donor pool of low-income and lower-middle-income countries, it constructs a synthetic counterfactual to depict the economic trajectory in the absence of conflict. The analysis reveals a significant decline in national gross domestic product (GDP) per capita, estimated between 45.3 percent and 47.8 percent over a decade of conflict, resulting in a cumulative GDP loss of US$29.7 billion to US$32.4 billion (purchasing power parity, PPP, adjusted). Two model specifications are employed, one using pre-treatment outcomes and the other integrating external covariates. Robustness checks support the findings, indicating a minimum 10-year decline of 35.3 percent in GDP per capita. Even considering the 2003 coup, this civil war has the most detrimental economic impact. The analysis remains robust when incorporating GDP data from remote sensing sources. These effects align with the fragility trap concept, portraying one of the highest economic impacts of civil conflict in terms of relative GDP per capita decline.
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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 | 0.001 |
| 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 it