Bibliographic record
Abstract
In their book, Debt, the IMF, and the World Bank: Sixty Questions, Sixty Answers, Eric Toussaint and Damien Millet provide an extensive overview of the impact that debt has on highly indebted poor countries (HIPCs).They show how debt inhibits HIPCs from investing in infrastructure, health, education, and other development-oriented socialpolicies.The problem, they argue, is not just debt itself, but the capitalist global economic system that fuels it.This process contributes to an endless cycle of poverty and human suffering.The book is written for a general audience.It is organized in a way that showcases the causes of the cyclical debt crisis, the major actors involved, and ways of addressing the problems.It is especially proficient in outlining how private actors in the global financial system have had a negative impact on HIPCs.Throughout the book Toussaint and Millet display Marxists overtones.Their critique of the global financial system and the exploitive nature of the global trade system is accurate.They provide some "radical alternatives" to the current, broken system.However, many of their ideas are not overly radical.For example, their argument focusing on the elimination of structural adjustment programs (SAPs) is in line with many other experts in the field.In fact, this discussion on SAPs is the highlight of the book.Toussaint and Millet give an in depth explanation of the role SAPs have played in this debt crisis.They describe them as entities geared toward developing institutions and policies to allow private foreign investment into countries.This process shapes the structure of the domestic political economic policy in debtor countries.It gives lenders
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.010 |
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".