Bibliographic record
Abstract
The share of the richest 1 % in total pre-tax income has increased in most OECD countries in the past three decades, particularly in some English-speaking countries but also in some Nordic (from low levels) and Southern European countries. Today, they range between 7 % in Denmark and the Netherlands up to almost 20 % in the United States. This increase is the result of the top 1 % capturing a disproportionate share of overall income growth over the past three decades: up to 37 % in Canada and even 47 % in the United States. This explains why the majority of the population cannot reconcile the aggregate income growth figures with the performance of their incomes. At the same time, tax reforms in almost all OECD countries reduced top personal income tax rates as well as rates of other taxes affecting the highest income earners. The crisis did put a temporary halt to these trends – but it did not undo the previous surge in top incomes. In some countries, top incomes had already largely recovered in 2010. To respond to these trends, governments have several options at hand to increase effective taxation paid by top income recipients without necessarily raising their marginal rates, to improve tax compliance and to reduce tax avoidance. Inequality and policies to restore equal opportunities have moved to the forefront of the political debate in many countries. Topping the bestseller lists is Thomas
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.342 | 0.178 |
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; the direct Gemma label and the distilled Codex classifier 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".