Ontario's cattle kingdom: purebred breeders and their world, 1870-1920
Bibliographic record
Abstract
Based on abundant original research linking science, agriculture, business and the state, Ontario's Cattle Kingdom explores the significance of beef cattle and livestock farming in Ontario during the late nineteenth and early twentieth century. Margaret Derry concentrates much of her research on the herds themselves (purebred and otherwise), using them as cultural texts to explain patterns of innovation adoption and the problems with strategies to control market share. The result is a fascinating and lively work, illustrating the complexity of agricultural history and offering an entirely new perspective on the social history of post-Confederation Ontario. The story of the purebred cattle breeders' world, for example, also describes the medical opinions of the nineteenth century, as well as disease control and the relationship between human and animal illness. And the stories are many: the evolution of cattle associations and organizations, the impact of technological progress on purebred herds, attempts to control disease and state regulation, and the relationship between the producers and consumers. Drawing from a wealth of historical case studies, Derry also presents the purebred breeders' theories and practices, their views on genetics and eugenics, as well as the implications of these practices on national and international patterns of beef economy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".