Evaluating food hubs: Reporting on a participatory action project
Bibliographic record
Abstract
Food hubs create a range of economic, social, and environmental impacts through a wide variety of activities and programs. Evaluation of these impacts is important; however, many hubs lack the capacity (including time, resources, knowledge, and expertise) to do effective, ongoing evaluation work. This lack of capacity is exacerbated by the difficul¬ties inherent in capturing the kinds of complex, multidimensional, context-specific impacts and outcomes that many of these businesses and organizations strive to achieve. This paper reports on a participatory research project designed to develop a resource to support food hub evaluation efforts. It presents highlights from the guide that was created and discusses associated insights regarding the tensions and opportunities of food hub evaluation. We argue that food hubs need to be engaging in evaluation efforts, even in the face of significant resource constraints, as a means of strengthening individual entities and the sector as a whole. These efforts must be carefully aligned with a hub’s stage of development and context-specific, multifunctional goals. They should also account for food hubs’ emergent, dynamic, and adaptive nature. To that end, participatory evaluation methodologies that take a flexible, collaborative, action-oriented approach are especially relevant.
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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 | 0.111 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".