Bibliographic record
Abstract
Open Food Network is a worldwide collaborative network of projects that want to change local food systems and boost short distribution circuits by means of open source software and democratic governance. It forms a global community with projects based in Belgium, Australia, UK, France, Canada, USA, Spain and Portugal, and which is still growing. A single global team develops an online marketplace for local food enabling independent online food stores connect farmers and food hubs with individuals. It gives them an easier and fairer way to distribute their food. This boosts short distribution circuits which positively impact local communities. Each of the local projects offer the software as Saas to their local communities who participate on the governance of the platform taking part on the decision making. The project is sustained by the funds contributed by all the local projects that are communalized. This makes it possible to afford the development of such ambitious project, which would be impossible by each of the local communities while still allowing new local projects to join. This talk aims to share Open Food Network's challenges around its governance and internal organization. How it successfully manages to coordinate the efforts of people scattered throughout the world while involving local communities on its design. We also touch on the tech challenges around the tech stack and infrastructure and how we envision it in the future.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.306 | 0.263 |
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".