Cultivating Community: Local Business People and Family Farmers Sharing Values and Mutual Support
Bibliographic record
Abstract
Imagine living in a house where there is no food. This house has no pantry, no cupboards, indeed, no kitchen either. Three times a day a truck arrives to deliver ready-made food. Think how vulnerable you would be if a glitch occurred in the system, how hungry you’d get, waiting for it to be fixed. Imagine how frightened you’d be if you suspected someone had access to the system who did not have your health and well-being in mind (or worse, looked upon you as an enemy.) Think of how you would never have that cozy, secure feeling you get when someone is baking or cooking a nice stew. Crazy, right? For house substitute community, and for kitchen substitute farm. Imagine how it would be to live in a community with no farms. We’d be 100 % dependent on trucked-in food and we’d never see cows grazing, carrots growing, hay being mowed, or any number of farm scenes. Our children would not have the opportunity to visit the new lambs or discover the joys of digging potatoes from the earth, as if discovering gold. We’d be totally dependent on a food system over which we had no control. Building Community Obviously you’re not all alike and I don’t know all of you but the fact that you’re living in a small town, in a rural state, that you run a business, and care enough to come to a Chamber of Commerce meeting already tells me a lot. I suspect that most of us here care about the well-being of the community, the beauty of the
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".