THE USDA GRADUATE SCHOOL: GOVERNMENT TRAINING IN STATISTICS AND ECONOMICS, 1921–1945
Bibliographic record
Abstract
The USDA Graduate School was founded in 1921 to provide statistical and economic training to the employees of the Department of Agriculture. The school did not grant degrees, but its graduate courses were accepted for credit by a significant number of universities . In subsequent years, the activities of the school grew rapidly to provide training in many different subject areas for employees from almost all federal departments. The training in statistics provided by the school was often highly advanced (instructors included Howard Tolley and, later, Edwards Deming), while the economics taught displayed an eclectic mix of standard and institutional economics. Mordecai Ezekiel taught both economics and statistics at the school, and had himself received his statistical training there. Statistics instruction in 1936 and 1937 included seminar series from R.A. Fisher and J. Neyman, and courses on the probability approach to sampling involving Lester Frankel and William Hurwitz became important after 1939. The instruction in economics was noticeably institutionalist in the period of the New Deal. Towards the end of the period considered here, the instruction in economics became narrower and more focused on agricultural economics. The activities of the school provide a basis for understanding some of the sources of the relative statistical sophistication of agricultural economists and of the statistical work done in government in the interwar period. It is noteworthy than within the USDA Graduate School, and in contrast to the Cowles Commission, statistical sophistication coexisted with an approach to economics that was not predominantly neoclassical. It also provides a light on the place of institutional economics in the training of government economists through the same time span.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".