‘… Silver in the Stars and Gold in the Morning Sun’: Non-farm Rural Landowners' Motivations for Rural Living and Attachment to their Land
Bibliographic record
Abstract
Studies have identified that, given the opportunity, the majority of North Americans would prefer to live in small towns and rural areas. This preference is based in aesthetic notions linked to landscape features, personal meaning, and perceptions. In order to understand how the growing non-farm rural landowner population will influence the rural landscape, this research explored the motivations of non-farm rural landowners for living in rural areas, and their perceptions of their property. It involved five preliminary focus groups with farm and non-farm landowners owning land in rural, urbanising rural, and urbanised rural areas, and four final focus groups. The research also included a survey of 944 landowners in Southern Ontario. People choose to live in rural areas because they are quiet, natural, open, private, and clean. In contrast, people chose to buy their properties for very practical reasons: location, cost, availability and quality of resources, and size. Results suggest that non-farm rural landowners prefer landscapes with trees and water, and landscape health, restorative benefits, and aesthetic quality are crucial. Associations with family, history, and activities provide the affective connection which supports ongoing efforts on their land.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".