David K. Thomson. <i>Bonds of War: How Civil War Financial Agents Sold the World on the Union</i>.
Bibliographic record
Abstract
The story of how the North financed the Civil War is an oft told tale. Among the many excellent accounts are those by Heather Cox Richardson, Leonard Curry, Robert Sharkey, and most recently, Roger Lowenstein. With a focus on the sale of bonds, David K. Thomson’s Bonds of War: How Civil War Financial Agents Sold the World on the Union adds important detail to this familiar picture. Thomson begins with an overview of the government loans that helped finance the American Revolution, War of 1812, and Mexican War. Typically, during the first half of the nineteenth century, members of the financial elite in the Northeast purchased these notes, and the amounts were modest. Bond sales during the Mexican War totaled $49 million. The Civil War presented the North with an unprecedented challenge. Once fighting began, Lincoln called for $400 million in funding to support an army of four hundred thousand. Eventually, over two million soldiers fought for the Union, and the cost of the conflict ballooned to $3.2 billion. Bond sales accounted for about two-thirds of that total, or more than $2 billion. The rest came from the tariff, fiat paper money, an income tax, and sundry duties.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 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".