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Record W3124788691

Agricultural Improvements and Access to Rail Transportation: The American Midwest as a Test Case, 1850-1860

2009· preprint· en· W3124788691 on OpenAlexaboutno aff
Jeremy Atack, Robert A. Margo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural economicsRevenueCensusAgricultureQuarter (Canadian coin)ProductivityGeographyAgricultural landCapital (architecture)Land useEconomicsBusinessEconomic growthEngineeringArchaeologyFinance
DOInot available

Abstract

fetched live from OpenAlex

During the 1850s, land in U.S. farms surged by more than 100 million acres while almost 50 million acres of land were transformed from their raw, natural state into productive farmland. The time and expense of transforming this land into a productive 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 barely less than one-eighth of the land area of the country at that time. Using a new GIS-based transportation database linked to county-level census, we estimate that at least a quarter (and possibly two-thirds or more) of this increase can be linked directly to the coming of the railroad to the region. Farmers responded to the shrinking transportation wedge and rising revenue productivity by rapidly expanding the area under cultivation and these changes, in turn, drove rising farm and land values.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.825

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.010
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2009
Admission routes1
Has abstractyes

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Same topicAmerican Environmental and Regional HistoryFrench-language works237,207