In the Midst of Alarms: The Untold Story of Women and the War of 1812. By Dianne Graves. (Toronto: Robin Brass, 2007. xvi, 495 pp. $44.95, ISBN 978-1-896941-52-3.)
Bibliographic record
Abstract
In the Midst of Alarms, by the Canadian historian Dianne Graves, is an outstanding study that significantly advances our understanding of women's lives in late eighteenth- and early nineteenth-century North America. Graves, an independent scholar, novelist, biographer, and the wife of the military historian Donald E. Graves, claims that when she explored her husband's archives, I realized that, although the diplomatic, political, military and naval aspects of [the War of 1812] had been extensively treated in print, there was relatively little about the life and times of the women who lived through it … In the Midst of Alarms is an attempt—from an impartial standpoint in terms of nationality—to bring to life the experiences of a broad cross-section of women in North America during the time of the War of 1812. (p. xi) Historians have long benefited from insightful studies of women during the early republic, including those penned by Linda Kerber, Mary Beth Norton, and Laurel Thatcher Ulrich. Further, military historians such as Michael N. McConnell and Richard Holmes have produced studies that extend the legacy of the new social history by examining eighteenth- and nineteenth-century military life “from the bottom up.” Graves skillfully blends both genres and provides a rich narrative that illuminates women's lives during a period of considerable stress, danger, and disruption.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".