National patterns of income and wealth inequality
Bibliographic record
Abstract
The aim of this article is to show that wealth must be treated as a distinct dimension of social stratification alongside income. In a first step, we explain why social stratification researchers have largely overlooked wealth in the past and present a detailed definition of wealth by differentiating it from income. In the empirical part of the article, we analyze the distribution of wealth across 18 countries, and we describe and compare national patterns of wealth inequality to those of income inequality making use of different data sources. Our results show – first – that there is strong variation in the distribution of wealth between these 18 countries, and – second – that levels of wealth inequality significantly differ from levels of income inequality in about half of the countries analyzed. Surprisingly high levels of wealth inequality we find in Sweden and Denmark, two countries widely considered being highly egalitarian societies. Conversely, the Southern European countries – where income inequality is relatively high – exhibit comparatively low levels of wealth inequality.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".