Growing Agricultural Communities in Northern Ontario: Lessons from Anabaptist Farmers
Bibliographic record
Abstract
Issues of food security are heightened in northern Ontario, as the agricultural activities are limited and networks for the distribution of fresh, local food tend to be informal and not widely utilized. Within northern Ontario, large pockets of underutilized agricultural land are available and some communities are experiencing an agricultural renaissance. Given the availability of land and relatively low land value, farmers from southern Ontario are moving to the north to rework old farmland and with this move, improving access to locally produced food. One particular community moving to the north are Anabaptist farmers from southwestern Ontario. More commonly referred to as Old Order Mennonites, and easily identified by their use of a horse and carriage for personal transportation, their migration to the north has resulted in the growth of six rural communities. Their motivation to move to northern Ontario is related to the abundance of large tracts of inexpensive farmland and future opportunities for their children to own farms; such opportunities were not deemed possible in southwestern Ontario. As a result of this movement, many northern communities now have access to fresh produce, such as melons and corn, not previously grown locally. While their successes in agriculture have come with challenges, northern communities and northern farmers can learn from their experiences to improve food security and access to locally produced, fresh food.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".