Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Economic and social shifts have led to rising income inequality in the world's affluent countries. This is worrisome for reasons of fairness and because inequality has adverse effects on other socioeconomic goods. Redistribution can help, but government revenues are threatened by globalization and population aging. A way out of this impasse is for countries to increase their employment rate. Increasing employment enlarges the tax base, allowing tax revenues to rise without an increase in tax rates; it also reduces welfare state costs by decreasing the amount of government benefits going to individuals and households. The question is: can egalitarian institutions and policies be coupled with employment growth? For two decades conventional wisdom has held that the answer is no. This book provides an assessment of the experiences of rich nations since the late 1970s. It examines the impact on employment of six key policies and institutions: wage levels at the low end of the labor market, employment protection regulations, government benefit generosity, taxes, skills, and women-friendly policies. The analysis includes twenty countries, with a focus on Australia, Canada, Denmark, Finland, France, Germany, Italy, the Netherlands, Norway, Sweden, the United Kingdom, and the United States. The book concludes that there is some indication of tradeoffs, but that they tend to be small in magnitude. There is no parsimonious set of policies and institutions that have been the key to good or bad employment performance. Instead, there are multiple paths to employment success. The comparative experience suggests reason for optimism about possibilities for a high-employment, high-equality society.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it