Locality, Personal Ties, and Efficiency in a Food Security Network
Bibliographic record
Abstract
Food sharing and distribution organization systems provide critical resources for local communities and food-insecure households. In this article, we investigate a newly collected data set of the Thrive Network in southwestern Virginia, which links forty food security organizations through fifty-one connections, using a theoretical framework of organization science within geographic space. We first test whether more central and higher degree organizations are toward the geographic center of the network, near convenient points of interest. We then measure whether organizations are likely to form connections based on nearness and logistical effectiveness of moving goods, and where this occurs. Finally, we find “missed connections,” defined as sets of organizations that are nearby but highly disconnected in the network. We find that important nodes are not necessarily in the center of the network, but are located on the periphery, and that relatively few organizations are connected to their nearby neighbors. We find that important nodes are not necessarily in the center of the network, but are located on the periphery, and that relatively few organizations are connected to their nearby neighbors. As such, this system could be predicated on bottom-up personal relationships rather than a hub-and-spoke supply chain configuration, and new ties might make the system more effective. We use our findings to help the Thrive Network build a more resilient food-sharing system and better serve vulnerable clients.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".