Carsey Perspectives: Bridging Farm and Table: The ‘Harvest to Market’ Innovation
Bibliographic record
Abstract
In this perspectives brief, authors Andrew Walters and Edie Allard describe a new online platform, Harvest to Market, that makes it easier for small farmers to sell their products directly to local consumers. Consumers visit the Harvest to Market website and find “markets” in their area. A market in this virtual context is a group of one or more nearby farms that collaborate online under the guidance of a coordinator called a market partner. The market partner, who can be a conscientious consumer, a farmer, a member of a local agricultural organization, or any other engaged individual, creates a web page for the online market using the Harvest to Market website and tools. Harvest to Market currently hosts approximately 23 active online markets throughout Maine, New Hampshire, and Vermont. The website acts as a catalogue of all online markets and individual farms, allowing potential buyers to search for exactly what they are looking for and to support local farms in the process. As it grows, Harvest to Market has the ability to have a transformative impact, encouraging local people to eat local food and encouraging small farmers to keep growing healthy produce. The exchange nurtures the connection between farmer and consumer.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".