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.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".