The 25% Shift: The Benefits of Food Localization for Northeast Ohio and How to Realize Them
Bibliographic record
Abstract
The local food revolution has come to Cleveland—big time. The city now has so many community gardens, farmers markets, community-supported agriculture (CSA) subscriptions, urban farms, celebrity chefs, and local-food procurement programs that the environmental web site, SustainLane, recently ranked Cleveland as the second best local-food city in the United States. But the region has only just begun to tap the myriad benefits of local food.The following study analyzes the impact of the 16-county Northeast Ohio (NEO) region moving a quarter of the way toward fully meeting local demand for food with local production. It suggests that this 25% shift could create 27,664 new jobs, providing work for about one in eight unemployed residents. It could increase annual regional output by $4.2 billion and expand state and local tax collections by $126 million. It could increase the food security of hundreds of thousands of people and reduce near-epidemic levels of obesity and Type-II diabetes. And it could significantly improve air and water quality, lower the region's carbon footprint, attract tourists, boost local entrepreneurship, and enhance civic pride.Standing in the way of the 25% shift are formidable obstacles. New workforce training and entrepreneurship initiatives are imperative for the managers and staff of these new or expanded local food enterprises. Land must be secured for new urban and rural farms. Nearly a billion dollars of new capital are needed. And consumers in the region must be further educated about the benefits of local food and the opportunities for buying it.To overcome these obstacles, we offer more than 50 recommendations for programs, investment priorities, and policies. In a period of fiscal austerity, we argue, the prioritymust be to create "meta-businesses" that can support the local food movement on a cash-positive basis.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".