Probing the Financial Statecraft of Borrowers through Comparative Cases
Why this work is in the frame
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Bibliographic record
Abstract
This chapter describes the book's case study approach, which compares Ethiopia, Ghana, and Kenya. All three countries experienced the regional trend of increased borrowing from China and in international bond markets in the 2000s. However, the countries vary in strategic significance and donor trust, allowing for tests of heterogeneity in the financial statecraft of borrowers. The chapter discusses the data collection process for the case studies, with over 170 elite interviews, mostly with government and donor officials participating in aid negotiations, and how this data is used to trace debt-based financial statecraft in each country. The chapter briefly provides background on each country's political and economic context and previews findings on how their external finance portfolios impacted aid negotiations with traditional donors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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