Bibliographic record
Abstract
Tjalling C. Koopmans, research director of the Cowles Foundation of Research in Economics, was the first US economist after World War II who, in the summer of 1965, travelled to the Soviet Union for an official visit to the Central Economics and Mathematics Institute of the Academy of Sciences of the USSR. Koopmans left hoping to learn from the Soviet economists’ experience with applying linear programming to economic planning. Would his own theories, as discovered independently by Leonid V. Kantorovich, help increase allocative efficiency in a socialist economy? Inspired by a vague notion of universal reason spanning the iron curtain, Koopmans may have even envisioned a research community that transcends the political divide. Yet, he came home having discovered that learning about Soviet mathematical economists might be more interesting than learning from them. On top of that, he found the Soviet scene caught in the same deplorable situation he knew all too well from home: that mathematicians are the better economists. Reconstructing Koopmans’s voyage from a first-person perspective puts the spirit of universal economic knowledge at Cowles to test: Is it capable of establishing a dialogue across the political divide of the Cold War or is it limited to the Western academic cocoon?
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".