Bibliographic record
Abstract
The prevailing ideology in Ontario at the time was a conservative culture that rejected everything American and attempted to preserve the best of the British world in the new Eden. Those building the state believed that a social and political hierarchy composed of those possessing a "natural virtue" would serve society best. In consequence, a few individuals at the top of the hierarchy, through their access to power, came to control the bulk of the land, the basis of the economy. At the other end of the spectrum from the elite were those transforming the land and themselves through their own labour. How did the physical environment and government land policy affect the pattern of settlement and the choice of land for a viable farm? What was the price of land, and how common was credit? Did the presence of reserved lands hinder or promote development? How extensive was land speculation and how did it operate? Clark brings these issues and more to the forefront, integrating concepts and substantive issues through a problem-oriented approach. Blending qualitative and quantitative approaches, he weaves together surveyors' records, personal and government correspondence, assessment rolls, and land records to measure the pulse of this pre-industrial society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".