Bibliographic record
Abstract
Although a great deal of Michael Wallerstein's work concerns the inequality of income, wages, and wealth, very few of his papers contain significant normative claims beyond those of economic efficiency. Of course, the fact that Wallerstein eschewed any substantive normative discussions in his work does not imply that he held no views on the fairness or otherwise of particular patterns of wage and income inequality; far from it. Rather it reflects his belief that, before any compelling normative case can be made regarding any given distributions of income or wealth, it is necessary to understand as deeply as possible exactly how such distributions come about. And while there are a great many reasons offered in the literature for levels and changes in income inequality, Wallerstein focused on exploring how political-economic institutions influence and support particular distributions of income. In the early 1980s, the ratio of wages earned by those in the 50th percentile of the wage distribution to those in the 10th percentile was 1.96 in the US, 1.64 in France, and 1.31 in Sweden. By the late 1990s, the ratios were 2.1, 1.59, and 1.39, respectively. Over the same period, evidence from a set of eighteen OECD countries (including the US, France, and Sweden) indicates, first, a strong negative correlation between pretax income inequality and government spending on “safety net” insurance against loss of income and, second, a negligible relationship between pretax income inequality and any purely redistributive government spending in favor of the poor.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.384 | 0.193 |
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".