Bibliographic record
Abstract
Within a week of his 20 January 2025 inauguration, US President Donald J. Trump issued an order that froze all federal grants and loans, creating confusion and anxiety about the future of research and development in US biomedical science. The politicisation of science creates significant challenges not only for the researchers who depend on public funding to undertake their research, but also for the public understanding of why basic research is so important to the health and economic prosperity of the world's ageing populations. In 1944 US President Franklin D. Roosevelt wrote a letter to the director of the Office of Scientific Research and Development, Dr. Vannevar Bush, asking Bush how science and medicine could be best harnessed to win the war of science against disease. Bush's response, in his acclaimed 1945 book entitled Science, The Endless Frontier, detailed how 'scientific capital' determines the pace and shape of technological progress. The war against disease approach to public health and medicine has helped increase life expectancy, by reducing the prevalence of premature death, but it has also contributed to the increasing global healthspan-lifespan gap, which is nearly 10 years. Translational gerontology, and in particular the goal of developing geroprotective drugs that may help fortify the 'biological resilience' needed to increase healthy life expectancy, must become an integral part of a 'wisdom-inquiry' approach to public health and medicine if the aspiration of healthy longevity is to be realised this century.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".