Bibliographic record
Abstract
As urbanization rates around the world continue to climb, especially in developing nations, the agricultural sector is being left behind. It is necessary to examine the direct and indirect effects that urbanization has on farmland surrounding mega cities, and how local governments can adapt to avoid conflict. Urbanization and urban sprawl take over arable farmland in order to start development of housing, commercial, or recreational land. These new developments also hike up the land price of local small-scale farms in the area and make owning land an extreme privilege. The main goal of this paper is to establish the relationship between urbanization and agriculture, and examine its effects on the sector, via farmland loss, land price, and food costs. Many farms around major cities have a continuous loss in the number of farms in their vicinity. In addition to this, the ownership of these agricultural plots is decreasing, as small-scale farms are only able to rent the land. Results led to a conclusion that urbanization and urban sprawl do in fact have a serious effect on the agricultural sector that surrounds the city limits, and recommendations such as the Greenbelt Plan that took effect in 2005 should be implemented in cities suffering from this issue Through much debate in the early 21st century, the establishment of the Greenbelt Plan, which protects farmland and greenspace around the city of Toronto acted as a massive success, and one that can be viewed as a proper mitigation outcome. This implementation of a Greenbelt is a proper solution in allowing for both agricultural lands, the ecosystem and biodiversity to thrive in the face of urbanization. Studying the impact of the Greenbelt is especially important in developing countries, as well as countries currently experiencing problems with urbanization and urban sprawl. The cases of Turkey and Pakistan experienced much of the same issues Toronto saw in the early 21st century and are now struggling to identify mitigation solutions to stop the spread of arable farmland into developments such as housing and commercial. The agricultural sector is slowing decreasing in number of farms as family owned, and small-scale operations are getting more and more difficult to operate around the world, especially in the vicinity of large cities experiencing a population boom.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".