Digital technologies in local agri-food systems: Opportunities for a more interoperable digital farmgate sector
Bibliographic record
Abstract
Agriculture e-commerce technologies are transforming how small and medium-scale farmers distribute food, consumers access local food, and market vendors negotiate sales. However, most of the social scientific literature exploring digital agriculture concentrates on big data analytics in the context of commodity farming systems and conventional supply chains. In this paper we review the social scientific literature on agriculture e-commerce technologies and situate this literature within broader debates over digital agriculture and its uneven social and economic dynamics. We find that most social scientific literature does not include agriculture e-commerce in its definition of digital agriculture, instead defining it predominantly in terms of production (e.g., variable-rate technology) or verification (e.g., blockchain) technologies. We contextualize this review with results from a series of focus groups exploring the challenges faced by Ontario's “digital farmgate sector”—the suite of agriculture e-commerce platforms that organize local food sales for hubs, farmers' markets, and small- and medium-scale farmers—related to lack of platform interoperability. We find that local food systems actors are increasingly adopting e-commerce platforms, particularly in the context of the pandemic, and observing substantial business-related benefits to their adoption. Yet, there are common frustrations with digital tools due to market fragmentation and lack of platform interoperability. We recommend the collaborative development of an open standard for e-commerce platforms that allows for the cross-platform sale of local food and farming products.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".