Bibliographic record
Abstract
The aim of this discussion is practical; otherwise it largely repeats some very general observations, chiefly historical and philosophical.I boast no expertise in anything specifically medical, to do with either medical care or medical administration.My concern is with the system of medicine and with the ethical and social issues that it involves.1 Applied philosophy is a still uncharted territory.Philosophers traditionally focus more on justifying accepted solutions than on seeking new solutions to urgent or interesting problems: their staple problems are those of justification.Thus, they spend less time discussing scientific problems and difficulties, and more time discussing the justification of the claims that we know.They habitually choose the simplest and most unproblematic claims for knowledge, and then try to substantiate them.As the famous writer William Somerset-Maugham has observed, from reading philosophy one might get the impression that there is no pain more important than my toothache.Possibly this makes sense: philosophers discuss the justification of the claims that are most accessible, since if they cannot justify them they cannot justify anything.This is indeed the case: even to justify that I have toothache or that I see a desk before me is impossible.Philosophers often find this tormenting.And perhaps it is.Yet
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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".