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Record W4405268216 · doi:10.1017/9781009475075.004

Probing the Financial Statecraft of Borrowers through Comparative Cases

2024· book-chapter· en· W4405268216 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2024
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Regulation and Crises
Canadian institutionsConcordia University
Fundersnot available
KeywordsBusinessEconomicsFinancial system

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.225
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it