Bibliographic record
Abstract
Eugen Weber makes a daunting subject for an academic obituary not because he was opaque, obscure, or even controversial, but because he was so well known, indeed popular. At his home institution of UCLA he was a celebrity professor. Upon his death, praise and recollections marked pieces from the International Herald Tribune, the Guardian, Le Figaro, Le Monde, the Los Angeles Times, the New York Times, the Washington Post and the Star (Toronto). Weber, after all, had his name emblazoned on t-shirts, had a popular reading audience and was a video star, hosting his own public television lecture series as éminence grise for The Western Tradition. He managed this in an age when the distinguished European elder gentleman with pipe in hand (not a prop for him) and a stately com-mand of what appeared to be ‘civilization ’ was going distinctly out of fashion. He was, it is fair to say, somewhat at odds with many of the critical ‘turns ’ in historical scholarship over the last generations. I remember him from UCLA when I was a graduate student proposing to Carl Schorske that theory could be likened to George Lucas’s ‘Death Star’, destroying
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.627 | 0.463 |
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".