Bibliographic record
Abstract
There is a widespread belief that capitalism is responsible for the huge improvements in health that have occurred over the last century and a quarter. Capitalism is seen as the supreme engine of growth, and growth is seen as the crucial condition for health improvement. But it is not. Poor countries can and sometimes do have better health than rich ones. The US is held up as a ‘world leader’ in medicine when it is really a world leader in healthcare market failure, spending almost a fifth of its huge national income to produce overall health outcomes little better, and in some respects worse, than those of neighbouring Cuba, with a per capita income barely a twentieth as large. ‘Breakthroughs’ in health science and technology -- in nuclear medicine, genetic medicine, or nanotechnology -- are treated as triumphs of capitalist investment in research. But most innovative medical research is actually done in state-funded medical schools and research laboratories. In spite of the abundant evidence on all these points, the myth that ‘capitalism promotes health’ is consciously or unconsciously accepted by, probably, most people in the world. Disposing of it is the necessary starting point of any rational analysis.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".