Financial Reconstruction in Conflict and 'Post-Conflict' Economies
Bibliographic record
Abstract
This paper discusses some of the principal issues relating to the reconstruction of the financial sector in conflict-affected countries, focusing on currency reform, the rebuilding (or creation) of central banks, the revitalization of the banking system, and its prudential supervision and regulation. Different types of conflict have different effects on the financial system. Country priorities for reconstruction therefore vary accordingly. Nevertheless, the following problems repeatedly occur in reconstruction. First, central banks often remain weak and under-resourced. The consequence is haphazard and lenient supervision of the financial system, which is compounded by the frequently lax accounting and reporting standards of commercial banks. This hinders the application of international models of prudential supervision, such as the Basle Core Principles. Second, regulatory forbearance is common, reflecting both the technical weakness of central banks, but also the pressure of powerful interests—including war criminals—that straddle both state institutions and the financial sector. The consequences are leniency in the licensing of banks, insider-lending, excessive risk exposure, and a general failure to curb emergent bank crises. These in turn destabilize economies in recovery from war, and the fiscal burden of bank crises limits development and poverty spending—thereby threatening ‘postconflict’ reconstruction itself. – aid ; conflict ; financial development ; sub-Saharan Africa
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".