Bibliographic record
Abstract
Abstract In the introduction to Activity Analysis of Production and Allocation (Cowles Monograph No. 13), Tjalling C. Koopmans recalled that he developed the model of his “Optimal Utilization of the Transportation System” (in the proceedings of 1947 International Statistical Congress, which were reissued as an Econometrica supplement, 1949) “under the stimulation of statistical work for the Combined Shipping Adjustment Board, the British-American board dealing with merchant shipping problems during the second world war.” Similarly, the contributions of George B. Dantzig and Marshall K. Wood to Cowles Monograph No. 13 (two revised journal articles and five new chapters) emerged from wartime work for the US Army Air Force and postwar work for the Department of the Air Force. This article examines the context and consequences of the wartime roots of these foundational contributions to activity analysis and linear programming, with particular attention to Koopmans's 1942 memorandum for the Combined Shipping Adjustment Board titled “Exchange Ratios between Cargoes on Various Routes” (first published in his Scientific Papers, 1970).
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".