Bibliographic record
Abstract
The Roots of American Industrialization. By David. R. Meyer (Baltimore: Johns Hopkins University Press, 2003. Pp. xi, 333. Illustrations, maps. Cloth, $45.00)Geographer David R. Meyer published several seminal articles in the 1980s on the origins and the growth of the manufacturing belt in the Northeast quarter of the United States. Some of his conclusions are briefly summarized in the last chapter of the present book, where he shows how the Midwest seized a window of opportunity while the South was unable to jump onto the bandwagon. Now, focusing on the roots of eastern industrialization, he convincingly demonstrates the limits of the transition to capitalism interpretation.The East industrialized, Meyer contends, not because its agriculture was declining due to poor soils or competition from western products, but, on the contrary, because its farmers were prosperous and could constitute a market for the articles made in workshops and factories. This main argument is based on statistics of population: In 1820, when industrialization had already started, 89 percent of the people inhabiting the East lived in rural places (82 percent in southern New England, the most industrialized part of this region); twenty years later, the proportion was still 81 percent.According to Meyer, no full explanation of the puzzle of eastern antebellum industrialization may be based on interregional or foreign trade or on urbanization; it has to take into account the rural population and its main activity, agriculture. Far from being in bad shape, the eastern farmers were prosperous: they adapted themselves to the market (according to the famous Von Thunen rings) and produced surpluses that fed the urban residents and allowed some of their sons and daughters to migrate to the workshops and factories. They could do so even if they were not located near a canal because transportation by wagons on the turnpikes was competitive, except for low-value goods in bulk.Meyer doesn't simply intend to rehabilitate agriculture; his thesis is much more sophisticated. It is the close integration of agriculture and industry that may explain why the East became a manufacturing belt before the Civil War. The author weaves a fine fabric where technology, finance, business acumen, and social networks all play a role. The model is illustrated by case studies-geographical (metropolises, like Boston, New York, Philadelphia, and Baltimore, or states, like Massachusetts and Connecticut) and sectoral (textiles, shoes, hats, brass, clocks, etc). David Meyer has an acute sense of both chronology (his book is divided in two parts: before and after 1820) and locality. …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".