Bibliographic record
Abstract
At the October meeting of the Canadian Cardiac Society in Quebec City, which I was privileged to attend, the moderator, in introducing a panel, said that one invitee had responded that he liked the subject matter but would participate only if assured that the word "millennium" would not be used.While I applaud that doctor's point of view, at this moment I'm afraid it's not possible to discuss anything great or small without somehow acknowledging that the flow of time has led us to a kind of watershed, however artificial, not only between centuries but between millennia.Nevertheless, and notwithstanding that the third millennium doesn't really begin until the year 2001, it does seem a good time to contemplate where we have been and where we are going.While I fall somewhat short of dealing with millennia, I can call your attention to a book that is at once timely, scholarly, candid, and I believe largely accurate in appraising the current state of medicine and the social, political, and scientific currents of the past hundred years that have gotten us where we are today. The book is Time to Heal-American Medical Education from the Turn of the Century to the Era ofManaged Care by
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".