Bibliographic record
Abstract
Every year, a new cache of medical students is baptized into this ageless art, and with this distinction comes a ceremonial distribution of the white coat. I was exuberant when I received mine—how beautiful! The thick cords of fabric are so neatly woven, reminiscent of chain mail armor worn by Lancelot and his peers…. Yes, this is a suit of armor, my impervious battle dress against the foes of Disease and Illness. It is replete with knowledge, seething with confidence, keen in choice of diagnosis and treatment—a great garment of ritual and power no different than Merlin's robe or the shield of Constantine. The brilliant white threads reflect purity of heart and clarity of mind, illuminating the nature of human condition. With this armament, we saw ourselves as the natural optimistic extension of our time—purveyors of medical manifest destiny. I was confident a true understanding of humanity hinged on my education. What an astonishing machine, this Man creature! So intricate, so functionally efficient, so synergistic, the ultimate holistic wonder. And how very reasonable for such a pinnacle in the scheme of natural history to have insight regarding his mechanics, that he might tinker and prod, cut a bit here and there, a tincture of chemical now or again, polishing up the imperfections and peccadillos inherent in producing 6,000,000,000 copies of something, anything.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".