Bibliographic record
Abstract
Allan Bloom was born in Indianapolis in 1930. He studied at Chicago and at Paris and Heidelberg, and taught at many places, notably in Chicago's Basic Program in the 1950s, at Cornell in the 1960s, at Toronto in the 1970s, and at Chicago again from 1979 until his death this past October 7. I knew him as an undergraduate at Cornell from 1964 to 1968, as his colleague at Toronto from 1973 to 1979, again as his colleague at Chicago in the last year of his life, and as a frequent visitor in the years in between. It was as a teacher that my friends and I first encountered Mr. Bloom. The classroom was at the center of his life, and with him in it, it quickly moved to the center of ours. As a teacher Allan hit the ground running, enjoying his greatest successes at the outset. In just one semester at Cornell in 1962, he inspired two brilliant students to transfer to Yale, where he was to spend the following year. When he returned to Ithaca, so did they. In the one year at Yale, teaching in Directed Studies where the best undergraduates were to be found, he inspired two of these students to transfer to Cornell immediately, and another four to resolve to attend graduate school there. These decisions to transfer were made
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.011 |
| 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".