Bibliographic record
Abstract
Pierre-Gerlier Forest has put forward the case that we are on the brink of a revolution in health policy that will be the result of the interplay of five factors. I would not challenge any of them but would emphasize the need to address socio-economic health inequalities, which have the potential to become a major cost driver in a time of growing economic inequality. To Dr. Forest's list, I would add two important shifts that are taking shape. The first is the development of an outcome focus in healthcare that seeks to measure improvements in individual and population health status. The second is a Copernican revolution in which healthcare providers revolve around the patient. These developments will enable us to answer many questions about resource allocation and return on investment in healthcare, although I still think there will be an outstanding question of how many resources society is willing and able to allocate to healthcare.
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 | 0.021 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.022 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.045 | 0.050 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".