A spatial analysis of land-use change and agriculture in eastern Canada
Bibliographic record
Abstract
Publicly available statistics regarding rural demographics, rural society and land use are presented and analysed using Geographic Information System (GIS). Our aim was to provide a quantitative basis for discussion of rural policy issues such as urban encroachment. Productivity (tonnage) of crop agriculture has increased by about 230% over 40 years while that of livestock (expressed as livestock units) has remained constant. Agricultural consolidation and intensification seen in southern Ontario has not translated into economic sustainability where on-farm income declined from 1991 to 2000. However, on-farm income in southern Quebec and municipalities adjacent to Toronto increased, perhaps due to the niche markets created in these regions. The increases in agricultural activity throughout southern Ontario have occurred in regions that have been designated as sites for innovation clusters, thus providing a foundation of resources for bio-based industries to expand and innovate. Reflecting on trends of demographics and production systems we conclude that rural policy should orient its geographical delineation to regional and inter-provincial scales. Our analysis indicates that rural populations and communities are sustainable but agricultural enterprises have changed radically; they have maintained or increased productivity but lost profitability. Applying a rural landscape design to the entire region would help address the sustainability of agriculture and rural communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| 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".