Britain’s Wars with France 1793–1815 and Their Contribution to the Consolidation of Its Industrial Revolution
Bibliographic record
Abstract
The article discusses the economic consequences of Britain’s wars with France in the period 1793-1815. It seeks to answer two questions: Why did this warfare not substantially depress growth of GDP? and, What may have been its positive legacies and spinoffs for Britain’s economy? It argues that the costs of recruiting a substantial part of Britain’s labour force were fairly low as the manpower used by the armed forces to a considerable amount consisted of under-employed and unskilled labour. Changes in fiscal, financial and monetary policies financed the wars without crowding out investment in the wider economy. Agriculture responded positively to rising prices and managed to feed the growing population. Major industries — including the iron, coal, cotton and armament industries — built up new capacities in the hothouse conditions of the wartime economy. The upswing in expenditure and investment by the state helped to create favorable (geo)political conditions and prospects for expanding the export of services and commodities. In the long term, the outcome of the wars was a situation in which Britannia ruled the waves and retained large profits from building and supplying ships, shipping, marine insurance, banking, commercial, and entrepôt services for the rest of the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".