“A Country of Long Credits and Long Seasons”: The Federal Reserve Bank of Atlanta and the Agrarian Question
Bibliographic record
Abstract
A major contradiction in U.S. capitalism before the Federal Reserve Act of 1913 was a mismatch between the credit needs of agriculture and the financial resources of the country. Pitching the South and the West against the Northeast, this mismatch was indexed to the agricultural season as planting, harvesting, and marketing crops would lead to increased demands for currency and credit in the agrarian regions that caused outflows from financial centers and threatened the stability of money markets. A well-known historical dynamic that has seldom received critical theoretical attention, the problem of seasonality was particularly severe when it came to cotton. Focusing on the early years of the Federal Reserve Bank of Atlanta, I ask what it means to understand the historical geography of the Federal Reserve in light of the “agrarian question.” Attending to the particularities of capitalist development in agriculture and the racialized class relations of the Southern countryside, I draw on archival research to track the interventions of the Atlanta Bank aimed at better synchronizing the times of cotton and credit, 1914 to 1929, by providing seasonal liquidity and carrying banks through bad harvests and downward price spirals. Reinterpreted through the comparative lens of the agrarian question, these practices open a space to center processes of uneven development in the historical geography of the U.S. Federal Reserve System.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".