Bibliographic record
Abstract
This paper proposes a new model of wage determination and wage inequality. In this model, wage-setters set workers' wages; they do so either directly, as when individuals vote in a salary committee, or indirectly, as when political parties, via the myriad of social, economic, fiscal, and other policies, generate wages. The recommendations made by wage-setters (or arising from their policies) form a distribution, and all the wage-setter-specific distributions are combined into a single final wage distribution. There may be any number of wage-setters; some wage-setters count more than others; and the wage-setters may differ among themselves on both the wage distribution and the amounts recommended for particular workers. We use probability theory to derive initial results, including both distribution-independent and distribution-specific results. Fortuitously, elements of the model correspond to basic democratic principles. Thus, the model yields implications for the effects of democracy on wage inequality. These include: (1) the effects of the number of wage-setters and their power depend on the configuration of agreements and disagreements; (2) independence of mind reduces wage inequality, and dissent does so even more; (3) when leaders of democratic nations seek to forge an economic consensus, they are unwittingly inducing greater economic inequality; (4) arguments for independent thinking will be more vigorous in small societies than in large societies; (5) given a fixed distributional form for wages and two political parties which either ignore or oppose each other's distributional ideas, the closer the party split to 50–50, the lower the wage inequality; and (6) under certain conditions the wage distribution within wage-setting context will be normal, but the normality will be obscured, as cross-context mixtures will display a wide variety of shapes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".