“Between Worlds,” or an Imagined Reminiscence by Oskar Morgenstern about Equilibrium and Mathematics in the 1920s
Bibliographic record
Abstract
“Iwas born in 1902 in Görlitz, a small provincial town in Germany, and raised in Vienna, the great city of the multinational Austro-Hungarian empire. On my father's side, my family goes back to about 1530 in Saxony, my Lutheran forebears having been farmers, church wardens, judges, and businessmen. My mother was a natural daughter of Frederick III of Germany …” Yet another account of myself, for yet another encyclopaedia. Italian, this time. Once again, I put pen to paper and collapse the events of fifty years ago to a few familiar milestones. Now what shall I tell these Italians? “I finished the Gymnasium and took my Dr. Rer. Pol. at the University of Vienna in 1925. Awarded a Rockefeller Memorial Fellowship, I spent the next three years in England, the United States, France and Italy. Returning to Vienna, I soon became Docent, later Professor, at the University, and Director of the Austrian Institute for Business Cycle Research …” And then there will be the doctoral thesis,Wirtschaftsprognose, the other Institute, Princeton, and so on. It is remarkable really, the rehearsed inevitability of it all … So often have I gone through exercises of this kind that there are times when I even begin to believe them myself.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".