Social Class, Politics, and the Spirit Level: Why Income Inequality Remains Unexplained and Unsolved
Bibliographic record
Abstract
Richard Wilkinson and Kate Pickett's latest book, The Spirit Level: Why Equality is Best for Everyone, has caught the attention of academics and policymakers and stimulated debate across the left-right political spectrum. Interest in income inequality has remained unabated since the publication of Wilkinson's previous volume, Unhealthy Societies: The Afflictions of Inequality. While both books detail the negative health effects of income inequality, The Spirit Level expands the scope of its argument to also include social issues. The book, however, deals extensively with the explanation of how income inequality affects individual health. Little attention is given to political and economic explanations on how income inequality is generated in the first place. The volume ends with political solutions that carefully avoid state interventions such as limiting the private sector's role in the production of goods and services (e.g., non-profit sector, employee-ownership schemes). Although well-intentioned, these alternatives are insufficient to significantly reduce the health inequalities generated by contemporary capitalism in wealthy countries, let alone around the world.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".