Bibliographic record
Abstract
Dr. Roger B., M.D., graduated from the FCRH Honors Program in the spring of 1960 with a major in biology. He went on to become a Doctor of Medicine and Master of Surgery, graduating from the McGill University School of Medicine in Montreal in 1967. Dr. Roger saved many lives as a flight surgeon in the U.S. Marine Corps, and in 1972 established his own practice in Glens Falls North, New York. Reflecting upon his time at Fordham, Dr. Roger fondly recalls his junior year philosophy seminar with Dr. Quentin Lauer, the “Honors Scholars vs. Fordham Club Socialites” softball game, and his experience being in the first class to have access to Alpha House, the exclusive Fordham Honors building. He believes that the rigorous reading requirements and academic expectations of the honors seminars taught him how to study and think critically, and he attributes his success in medical school to the skills he learned in undergrad. Dr. Roger completed his senior thesis under the guidance of Dr. James Forbes, an entomologist. Roger’s interview was conducted by Kevin McKenna and Antonella Iannarino, FCRH honors class of 2007.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.349 | 0.206 |
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".