BRUCE VANDERVORT. Indian Wars of Mexico, Canada, and the United States, 1812-1900. (Warfare and History.) New York: Routledge. 2006. Pp. xvii, 337. $34.00
Bibliographic record
Abstract
In this thoughtful and well-crafted book, Bruce Vandervort focuses mainly on American Indian wars in the region west of the Appalachian Mountains from the War of 1812 to the end of the nineteenth century. Attention to Mexico and Canada is, in comparison, limited, and its findings stated with less confidence, but Vandervort writes in some detail about the second Seminole War, 1835–1842, in Florida—east of the Appalachian chain. The latter was a struggle that created a host of problems for the American military and, he argues, was the longest Indian war in the United States. The author maintains that the Indian wars were “an integral part of a global pattern of imperial conflict” (p. 19). Although the argument for imperial conflict in North America is unconvincing, one of the many great strengths of the book is that Vandervort places the Indian wars in wide perspective, comparing them with military confrontations elsewhere in the world. In the United States, however, imperial designs of the government played no significant role in the ultimate defeat of western Indians. Indeed, government troops sometimes, as in the case in Indian Territory, moved to protect Native Americans from whites. Moreover, westward-moving pioneers overran the West with such aggressive speed and in numbers large enough that Indian people, at least in the long run, could not compete, even absent larger imperial designs. In other words, with or without government protection or support in the form of a large army, such groups as traders, precious metal miners, railroad builders, farmer-settlers, and others, often carrying diseases to which Indian people had little or no immunity, brushed Native Americans aside. Indian people fought hard and well, as Vandervort shows, and they often gave the army and its soldiers more than the U.S. military (or the Canadian or the Mexican armies) could handle, but, again, in the United States Anglo population numbers alone meant that Indian people, without government support and treaty guarantees, had no chance to hold on to their large domains. Contemporaries knew it; cattlemen especially understood it, and thus many of them for selfish reasons, such as grazing opportunities, supported Indian efforts to preserve Native American lands.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".