Bibliographic record
Abstract
Despite unprecedented investments in public health and biomedical research, improvements in life expectancy and healthy life expectancy have stagnated in the United States. Part of the reason for this development can be traced back to the influence of "Protean" over "Post-Protean" public health, the names that can be given to two contrasting visions of public health advanced in the early twentieth century. Protean public health prescribes "waging a war" against disease and was successful in reducing the early-life mortality risks from infectious disease. But Protean public health has proven less effective in improving the quality of life of older persons. Post-Protean public health prioritizes the experimental method and research into the indirect methods of improving health. It articulated a vision of public heath that was given a more concrete specification by Alex Comfort in what is now referred to as the Geroscience Hypothesis. To improve the health prospects of aging populations the dominance of Protean public health must be relaxed, to enable the benefits of Post-Protean public health to be realized. Doing so means shifting public health's aspirations towards increasing the healthspan vs "saving lives" by extending the duration of time older persons can survive by managing the multi-morbidities of late life.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".