Bibliographic record
Abstract
Eight years ago, my G6ttingen colleague Otto Gerhard Oexle and I were preparing a volume of essays in honor of my predecessor, Rudolf Vierhaus, for his 75th birthday.' We did not want to produce a medley of heterogeneous topics, so we had chosen a specific theme: essays from friends and colleagues of Vierhaus in which they would explain why, and under what circumstances, they had decided to study history in the late 1940s and early 1950s-why, in short, after experiencing the Third Reich in their teens and World War II as young men, they had decided to become historians. From the beginning of this project, it was my aim to provide a somewhat wider perspective than Vierhaus's German colleagues would be able to. Therefore I asked Fritz Fellner from Vienna, born in the same year as Vierhaus, to write a piece; the Swiss historian Walther Hofer; and Annelise Thimme, who had spent most of her life as a scholar teaching at Edmonton in Canada. And I also wrote to Hans Rogger. He answered promptly, very friendly, but not positively. He saw nothing special in his own path in becoming a historian, he argued. This was the answer I had feared. As we all know, Rogger was modest, too modest to talk about himself and about his scholarly achievements. But Rogger also added an argument that caused me to pause and ponder that perhaps I had made a grave mistake in asking him; perhaps I
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".