The influences of local environmental factors on settlement and agriculture in Saltfleet Township, Ontario, 1790--1890
Bibliographic record
Abstract
This thesis addresses the deficiencies of recent studies that principally employ cultural factors, such as religion and country of birth, to explain variations in wealth and property in nineteenth-century Ontario. Southern Ontario's "first land," an amalgam of particular environmental, climatic, and geophysical factors, presented settlers with a defined set of agricultural and economic possibilities. As settlement activities altered the natural surroundings, a new series of economic possibilities emerged, which in turn, required its own settler response. This relationship changed constantly. In Saltfleet Township, Ontario, the principle area of study for this thesis, the main economic activity was agriculture. A few decades of intensive farming negated the millennia required to enrich the soil with the matter necessary to sustain plant life, while deforestation exposed the ground to the eroding effects of rain and wind. These alterations required a change in the settlement landscape, broadly characterized as improved husbandry and crop specialization tailored to a farm's particular environmental characteristics. This combination of settlement and natural responses produced individual parcels of property with distinct characteristics, including soil fertility, climate, and distance to markets. Studies that seek to understand settlement and agriculture in southern Ontario cannot treat farmland as homogeneous, no matter how many qualifying statements are employed to acknowledge and then exclude these variations. In isolating local environmental variables, such as a farm's topography, drainage, distance to water, and location relative to the Niagara Escarpment, this thesis uses a new settlement model that emphasizes the importance of local environmental variations, the effect of which is only recognizable over a long period of time. What emerges is the importance of farmers' abilities to perceive changes in the land and market, and to act upon what they saw.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".