Introduction to the special issue of Japanese Political Economy on international income inequality
Bibliographic record
Abstract
This paper introduces the special edition on International Inequality. It contextualizes the papers against the background of the “new inequality literature”. This contains two broad strands: critics of the International Financial Institutions who focus on inequality between nations and those like Piketty who focus on with inequality within nations. These concerns have wider consequences, notably for scholars of the “Great Divergence” between the global South and North that followed the advent of capitalism; it is also theoretically damaging for neoclassical economics, whose standard models of Growth and Trade predict that the world market should reverse this Divergence. It identifies a number of lacunae in this literature. First, the literature does not address the relation between the two sources of inequality, though it is clear that the former has significant effects on the latter. Second, the literature does little to address the causes of inequality in general, confining itself to amassing a volume of empirical data. The contributions in this volume are dedicated to making good these absences. The introduction concludes with an analysis of the roots of the theoretical difficulties involved and calls for a concerted scholarly effort to construct superior alternatives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".