Bibliographic record
Abstract
Research on the determinants of inequality has implicated globalization in the increased income inequality observed in many advanced capitalist countries since the 1970s. Meanwhile, a different form of international embeddedness — regional integration — has largely escaped attention. Regional integration, conceptualized as the construction of international economy and polity within negotiated regions, should matter for inequality. This article offers theoretical arguments that distinguish globalization from regional integration, connects regional integration to inequality through multiple theoretical mechanisms, develops hypotheses on the relationship between regional integration and inequality, and reports fresh empirical evidence on the net effect of regional integration on inequality in Western Europe. Three classes of models are used in the analysis: 1) time-series models where region-year is the unit of analysis, 2) panel models where country-year is the unit of analysis, and 3) analysis of variance to identify how the between- and within-country components of income inequality have changed over time. The evidence suggests that regional integration remaps inequality in Europe. Regionalization is associated with both a decrease in between-country inequality, and an increase in within-country inequality. The analysis of variance shows that the net effect is negative, and that within-country inequality now comprises a larger proportion of total income inequality.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".