Review: <i>Grasslands Grown: Creating Place on the U.S. Northern Plains and Canadian Prairies</i>, by Molly P. Rozum
Bibliographic record
Abstract
Book Review| November 01 2023 Review: Grasslands Grown: Creating Place on the U.S. Northern Plains and Canadian Prairies, by Molly P. Rozum Grasslands Grown: Creating Place on the U.S. Northern Plains and Canadian Prairies. By Molly P. Rozum. (Lincoln, University of Nebraska Press, 2021. 500 pp.) John Mack Faragher John Mack Faragher Yale University Search for other works by this author on: This Site PubMed Google Scholar Pacific Historical Review (2023) 92 (4): 677–678. https://doi.org/10.1525/phr.2023.92.4.677 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation John Mack Faragher; Review: Grasslands Grown: Creating Place on the U.S. Northern Plains and Canadian Prairies, by Molly P. Rozum. Pacific Historical Review 1 November 2023; 92 (4): 677–678. doi: https://doi.org/10.1525/phr.2023.92.4.677 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentPacific Historical Review Search In this important study, Molly P. Rozum, professor of history at the University of South Dakota, offers a deeply researched examination of the first generation of settlers to grow up on the northern grasslands of North America—the states of Minnesota, the Dakotas, and Montana, and the provinces of Manitoba, Saskatchewan, and Alberta—at the turn of the twentieth century. Rozum asks how these “grassland-grown” men and women developed a specific sense of place and regional identity. Her research breaks new ground, but it also strains against the limits of her sources and her questions. She interrogates the writing—both published and unpublished (from a score of archives)—of several dozen individuals, more than half of them women, born the offspring of American, Canadian, and immigrant settlers during the half-century from 1860 to 1910. She refers to them as “settler colonials” to distinguish them from the Indigenous inhabitants. They do not, however, constitute a... You do not currently have access to this content.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.025 |
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".