The New Decision-Makers in the Rural Landscape – Who Are Non-Farm Rural Landowners?
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. This research investigated the rural non-farm landowner of Southern Ontario in order to describe their characteristics. 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 with non-farm rural landowners. The research also included a survey of 944 landowners in Southern Ontario. This mix of quantitative and qualitative data collection was intended to support a minor level of prediction (what will happen in the future as a result of the impact of this group?); process (working with the group under study to develop solutions through a research partnership); and generalization (as much as possible through the combination of methods). Study results suggest that the number and proportion of retirees and professionals in rural areas are increasing, and 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. Non-farm landowners should be considered separate and distinct from farmers, as they have different backgrounds, education levels relating to land use, and connections to the land. Policies and decisions relating to development and preservation need to treat these groups as having different priorities and perspectives on the issues. 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 for non-farm rural landowners.
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".