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Bibliographic record
Abstract
What’s the issue? In all countries, it is well-established that poorer people have substantially shorter life expectancies and more illnesses than the rich. This phenomenon has been observed since at least the nineteenth century when Chadwick (1965) investigated the health of the working classes in Victorian England. Two contemporary British studies have been very influential in their documentation of the relationship between socioeconomic factors and health status. The Whitehall civil service study compared the health status of individuals over time with their position in a well-defined job hierarchy. Those lower in the hierarchy experienced three times the risk of death from heart disease, stroke, cancer, gastrointestinal disease, accident and suicide compared with those at the top of the hierarchy. These differences could not be explained by differences in medical care. The Secretary of State for Health in Britain was concerned about why – 30 years after the establishment of the National Health Service (which made health serves available to all, regardless of income) – significant differences in mortality between social classes persisted. The Black Report, released in 1980 (Townsend et al., 1992) concluded that these differences in health status were not the result of individual differences, but rather of structural differences in the way members of these different classes led their lives. This included a wide
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.809 | 0.707 |
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; the direct Gemma label and the distilled Codex classifier 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".