Bibliographic record
Abstract
Abstract This innovative book shows that the health, education, and employment of billions of people have been improving on every continent in the past three decades. The globalization of welfare has had the biggest impact in developing countries, where more than five-sixths of the world’s population live. In Africa, Asia, Latin America, and the Middle East there has been great progress in eradicating infant mortality and illiteracy, people are living longer, and more youths have a chance to get a secondary education. These achievements are the product of a welfare mix combining resources of the household, the market, and the state. Given low starting points, only a minority of developing countries have already caught up with the high standards of welfare in Europe, the United States and Canada, and the Asia Pacific region. Slow but steady rates of progress show that people in a majority of developing countries can expect to catch up with the high, fixed standards of welfare in the next three decades. This will happen sooner in China and later in India, because China has been unusually successful in using its resources to promote welfare, while India has been below the global average. These conclusions are based on the book’s systematic analysis of the Global Welfare Database, which combines official and unofficial data covering 95 per cent of the world’s population. The success of highly developed countries raises questions about how much is enough welfare. At what age will youths learn more by leaving classrooms and becoming employees? Is length of life or quality of life more important for older people? Should unpaid work caring for children and older family members have the same value as working and paying taxes in the official economy?
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; both teacher heads 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".