Review of <i>Policing the Great Plains: Rangers, Mounties,\nand the North American Frontier, 1875-1910</i> By Andrew R. Graybill
Bibliographic record
Abstract
At opposite ends of the Great Plains, the North-West Mounted Police and the Texas Rangers emerged in the mid-1870s as key instruments in the extension of state power over distant frontiers. Policing the Great Plains reveals how these famous rural constabularies implemented policies designed in Ottawa and Austin to promote the settlement and economic development of the Great Plains. Andrew Graybill argues that these shared political and economic goals ensured that Mounties and Rangers, despite their many differences, helped bring about strikingly similar transformations in Texas and the Canadian Prairies.\nBy placing Mounties and Rangers in this common history of state and market expansion, Graybill redirects well-worn stories of 140unties and Rangers into more fruitful avenues of inquiry. Each of his four core chapters focuses on a particular stage in the state's absorption of its frontier and the role the constabularies played in that process. The first two consider the efforts of Rangers and Mounties to confine or remove Indigenous peoples and to dispossess people of mixed ancestry in order to appropriate Aboriginal lands and resources for the use of white farmers, ranchers, and entrepreneurs. The final two chapters explore how the constabularies helped to consolidate that new order. By defending cattlemen and ranching syndicates from the protests of the rural poor and helping mining and railroad corporations to suppress labor unrest, he argues, Rangers and Mounties played critical roles in consolidating the nascent industrial economy in the Great Plains. But these broad
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".