Bibliographic record
Abstract
1812: War and the Passions of Patriotism, by Nicole Eustace. Early American Studies. Philadelphia, Pennsylvania, University of Pennsylvania Press, 2012. xviii, 315 pp. $34.95 US (cloth). Nicole Eustace's 1812: War and the Passions of Patriotism explores several fascinating cultural themes associated with the Anglo-American war of 1812-15. They centre largely on how people in the United States during the early 1800s used concepts of gender and face both to unite family obligations with national responsibilities, and to articulate their understanding of the conflict's varied meanings. Her book stands proud of most scholarship on the war, which tends to address traditional military and political affairs (with respectable attention also being given to First Nations issues), although much of that body of literature is repetitive and often is not very good. While these older approaches always will enjoy currency, especially when new endeavours enhance knowledge, scholars also ought to pursue different questions to enrich appreciation of the conflict, particularly because interpretations of the war suffer from a narrower historiographical profile than do comparable traumas in North American history. As an original study of how Americans perceived the war and their place in the world, Eustace's effort represents a positive response to that need. Cultural history, of course, is a big field, especially within Eustace's parameters of gender and race, so she tends to focus on questions of feeling. Within that emphasis, she stresses how the conflict often was presented a romantic adventure ... in which dashing young men went to war to win the hearts of patriotic maidens and in which the thrill of romantic love contributed directly to the surge of patriotism (p. xiii). Thus it is no surprise that words like ardour, zeal, passion, and spirit lie prominently across the pages of her book. One example of interest in such matters is her discussion of how Americans employed language normally linked to courtship rather than combat to comprehend defeat (such as at Detroit), or to celebrate victory (as on Lake Erie). She also explores related subjects, including Malthusian debates between Americans and Britons--and between pro- and anti-war elements within the US--over population growth, the republic's lust for First Nations territories, and the preservation of slavery. Eustace mined an impressive range of sources for her study, including sermons, songs, and other under-appreciated texts, and her care in analyzing these records is obvious, as indicated, for example, by the nicely curated captions that accompany many of the images that grace the book. There are some unfortunate stylistic choices and factual errors that, to a small degree, undermine her efforts. …
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".