Bibliographic record
Abstract
Abstract The case for a modern democratic humane socialism typically has two parts. The first is that capitalism is bad, at or least not very good. In reaching this conclusion, most have either analyzed a theoretical ideal-type of capitalism or used a single country, often the United States, as a stand-in for capitalism. To fully and fairly assess democratic socialism’s desirability, we need to compare it to the best version of capitalism that humans have devised: social democratic capitalism, or what is often called the Nordic model. Each chapter in this book examines one of the things that we should want in a good society, that contemporary democratic socialists typically say they want, and that socialism might, conceivably, improve our ability to achieve: an end to poverty in rich countries, an end to poverty everywhere, more jobs, decent jobs, faster economic growth, inclusive growth, more public goods and services, affordable healthcare for all, helpful finance, truly democratic politics, economic democracy, less economic inequality, gender and racial equality, more community, and a livable planet. The book offers a close look at the evidence about how capitalist economies have performed on these outcomes, with particular attention to the performance of social democratic capitalism. The second part of the case for democratic socialism is the notion that it would be an improvement. For each of these outcomes, the book considers what, if anything, we can conclude about whether democratic socialism would do better than social democratic capitalism.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".