Our rural future?: The non-farm landowner and Ontario's changing countryside
Bibliographic record
Abstract
Rural areas are undergoing non-farm population growth as a result of various factors including changing lifestyle preferences, an aging population, and technological innovations which allow exurbanites to commute. Non-farm rural landowners own an increasing proportion of our rural land, but often have little knowledge or experience with land management, though they tend to be very interested in stewardship and conservation issues. This research investigated the rural non-farm landowner of Southern Ontario and attempts to describe their characteristics and explore the key themes and patterns which inform their landscape perceptions and priorities for action. It involved five preliminary focus groups with farm and non-farm landowners owning land in rural, urbanizing rural, and urbanized rural areas, and four final focus groups. The research also included a survey of 944 landowners in Southern Ontario. Study results suggest that the number and proportion of retirees and professionals in rural areas are increasing, and non-farm residents are more likely to live on or near their properties than in the past. Average property size has decreased, and education levels are increasing. The results highlight the importance of specific landscape types and features including topography, trees, wildlife and heritage architecture. Key aspects of landscape decision-making include the desire to influence or impact on their land, the need for visual or tangible results, concerns about aesthetic qualities, and the recreational and personal restorative benefits of conservation activities. These results provide information which will assist with the development of new initiatives, support the continuation of successful programs, and enable the tracking and assessment of new and continuing conservation and stewardship initiatives in Ontario.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".