Bibliographic record
Abstract
In this paper, a calculation of cost of the First World War to the United States is performed with the aim of evaluating the impact of the War on the American economy. The method used to make this calculation is based on the work of economic historians Claudia Goldin and Frank Lewis, who studied the cost of the American Civil War. This method involves the calculation first of the “direct cost” of the war, which represents the value of economic losses made up of war expenditures, casualties and the opportunity cost of drafted soldiers. The “indirect cost” of the War is also calculated to measure the impact of the War on American economic growth by projecting economic growth in a hypothetical world where the First World War did not occur and comparing it to the economic growth actually experienced in the United States. This calculation is meant to capture any positive effects that the War may have had. For the calculations, data was drawn from a number of primary sources including censuses and government documents. The results of both of these calculations show that the First World War had a negative impact on American growth and represented a massive drain of economic resources. In particular, the indirect cost calculation shows that American growth slowed considerably in the decade following the War. This result is significant as it contradicts the common view of the postwar period prior to the Great Depression as being one of great prosperity in the United States.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".