The Impact of Access to Rail Transportation on Agricultural Improvement: The American Midwest as a Test Case, 1850-1860
Bibliographic record
Abstract
During the 1850s, land in U.S. farms increased by more than a third—100 million acres—and almost 50 million acres, an area almost equal to that of the states of Indiana and Ohio combined, were converted from their raw, natural state into productive farmland. The time and expense of transforming this land into a productive agricultural resource represented a significant fraction of domestic capital formation at the time and was an important contributor to American economic growth. Even more impressive, however, was the fact that almost half of these total net additions to cropland occurred in just seven Midwestern states which comprised somewhat less than one-eighth of the land area of the country at that time. Using a new GISbased transportation database linked to county-level census data, we estimate that at least a quarter (and possibly two-thirds or more) of this increase in cultivable land can be linked directly to the coming of the railroad to the Midwest. Farmers responded to the shrinking transportation wedge which raised agricultural revenue productivity by rapidly expanding the area under cultivation and these changes, in turn, drove rising farm and land values.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".