Bibliographic record
Abstract
Abstract Stark inequality is a problem the world over, one that has been worsening over the past 30 years, particularly in rich, economically developed countries. To acquire the same amount of wealth as Elon Musk, the average American worker would have to work for more than four and a half million years. Is this inequality morally acceptable, and is it feasible to actually reduce inequality in the real world? This book makes the case for rejecting meritocracy, presenting a strong defense against the claim that individuals “deserve” their wealth. The book argues that people, especially rich people, do not morally deserve the bulk of their income because it does not, by and large, come from anything they themselves do but is largely thanks to the vast understructure of other people’s labor, in addition to their lucky possession of bodily talents and efforts. Furthermore, the book brings to light extensive historical and comparative evidence to show that raising taxes on both income and wealth is practically feasible and that any costs of doing so are far outweighed by the truly enormous benefits that such taxes could bring in terms of environmental sustainability, democratic equality, equal opportunity, and reduced racism and xenophobia.
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 | 0.005 |
| 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 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".