Loyalist Land Ownership in Upper Canada’s Norfolk County, 1792–1851
Bibliographic record
Abstract
After the American Revolution, many Loyalists moved north, where the British colonial government awarded them generous land grants on favourable terms. The intention behind these grants was to create a landed gentry in Upper Canada that would safeguard the colony’s political security and build social cohesion among its leadership. Loyalist Land Ownership in Upper Canada’s Norfolk County, 1792–1851 examines the long-term landholding of Loyalists and other settlers who arrived in the county before 1812 to judge whether this social experiment succeeded. Colin Read explores the various ways that settlers acquired and transmitted land, the nature of familial land sales, and the place of women in owning land. Consulting land records and genealogical research, he finds that no landed elite endured in Upper Canada: Loyalists owned only marginally more land than non-Loyalists by 1851, and it was commonplace for latecoming settlers to eventually own land. Yet early arrival was a significant determinant of later landholding and property size – it mattered who settled first. Land was the main source of wealth in early Canada. This fine-grained study sheds light on how it was acquired, disposed, and passed down through generations in the nineteenth century. Although a landed aristocracy was never realized, the colonial state’s allocation of land to settlers laid the foundation for their social standing.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".