Bibliographic record
Abstract
One of the greatest figures in the development of the dental profession to the high status it enjoys today is all but forgotten. Jonathan Taft was dean of the second dental school in the world and wrote the most important clinical textbook of his time, one that was reprinted in many editions over a quarter of a century. Later appointed dean of the new University of Michigan Dental School, he instituted innovations in admission requirements and course of study that were copied by all subsequent schools and are the standards adhered to today. The editor of one of the most important dental journals for 44 years, a record unmatched to this day, he set the standards for modern dental periodical literature that have done so much to elevate dentistry that today it stands on a par with medicine as a truly science-based profession. He served dentistry in many capacities: president of the American Dental Association, founder of the National Association of Dental Faculties, and founder of the National Association of Dental Examiners. Over his lifetime, he published almost 200 professional papers and probably attended and lectured at more dental meetings than anyone of his day and since. His memory should be resurrected, and the profession must be made aware of the great debt it owes to this intrepid fighter for a better dental profession.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".