THISARTICLE FOCUSES ON THE PROFESSIONAL LIFE and experiences of D.
Bibliographic record
Abstract
terviews conducted in the spring, summer, and early fall of 1998, Krummel discussed the persons, places, experiences, and ideas most influential to him as a veteran and historian of librarianship; as a professor and bibli-ographer; and as he progressed from undergraduate and graduate study in librarianship and music at the University of Michigan (1951-1956), to the Library of Congress (1956-1961), to the Newberry Library as head of reference and associate librarian (1962-1969). His remarks and sto-ries address the essence of libraries and librarianship. BACKGROUND Libraries, where one takes on the smell of books, stale and attrac-tive. Service with no motive, simple as U.S. Mail. Fountains and palms, armchairs for smokers. Incredible library where ideas run for safety, place of rebirth of forgotten anthems, modern cathedral for lovers. Library, hotel lobby for the unemployed, the failure, the boy afraid to go home, penniless. Switchboards for question-ers: What do you know about unicorns? How do you address a duchess? Palladian architecture of gleaming glass and redwood. Window displays of this week’s twelve bestsellers. Magnificent quar-ters of the director, who dines with names of unknown fame. Lava-tories, rendezvous of desperate homosexuals. In the periodical room the newspapers bound with a stick, carried like banners of
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".