Bibliographic record
Abstract
Abstract This chapter discusses the evolution of the distribution of earnings in recent decades in the US, Canada, and eastern and western Europe. The data show that the late 1960s and 1970s were a period of earnings compression in a number of countries (Finland, France, Italy, Sweden, and the United Kingdom); there was not a lull before the storm, and the falls in the bottom decile after 1980 can be seen as a part reversal of the 1970s compression. In many countries, there has been a steady upward movement since 1980 in the top decile (Australia, Canada, Germany, Italy, Portugal, Sweden, the United Kingdom, and the United States). The finding of a fanning out at the top is evident for Australia, Germany, Italy, Portugal, Sweden, the United Kingdom, and the United States. The three Eastern European countries all showed a move towards increased earnings dispersion with the transition to a market economy, but there are differences, with dispersion being less, and more stable, in the Czech Republic than in Hungary and Poland.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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; both teacher heads agree on what is shown here.
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".