Bibliographic record
Abstract
How should we assess the historical development of health care? Many historians are deeply reluctant to endorse ideas involving progress in human affairs, including the evolution of modern medicine. We tend to think either that our present situation is little better than in the past, or that most kinds of value judgments about history are subjective and inappropriate. A laudatory approach to medical history commonly adopted by "amateur" medical historians in the tradition of Sir William Osler has often been eschewed by "professionals" as faulty, feel-good history. But Osler was right in his belief that, on balance, the progress of medicine has been spectacular, that modern health care offers one of the finest examples of the possibility of "man's redemption of man." Written objectively, medical history is about progress and achievement, and can properly seen as inspiring. If we mordantly or relativistically dismiss the unprecedentedly high quality of modern health care, we lose the ability to understand why citizens value it so highly, and this distorts our understanding of current issues. We also lose our sense of the wonders of human and medical achievement.
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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".