A descriptive analysis of food pantries in twelve American states: hours of operation, faith-based affiliation, and location
Bibliographic record
Abstract
BACKGROUND: Our objectives were to describe both the development, and content, of a charitable food dataset that includes geographic information for food pantries in 12 American states. METHODS: Food pantries were identified from the foodpantries.org website for 12 states, which were linked to state-, county-, and census-level demographic information. The publicly available 2015 Food Access Research Atlas and the 2010 US Census of Population and Housing were used to obtain demographic information of each study state. We conducted a descriptive analysis and chi-square tests were used to test for differences in patterns of food pantries according to various factors. RESULTS: We identified 3777 food pantries in 12 US states, providing an estimated 4.84 food pantries per 100,000 people, but ranged from 2.60 to 7.76 within individual states. The majority of counties (61.2%) had at least one food pantry. In contrast, only 15.7% of all census tracts in the study states had at least one food pantry. A higher proportion of urban census tracts had food pantries compared to rural tracts. We identified 2388 (63.2%) as being faith-based food pantries. More than a third (34.4%) of food pantries did not have information on their days of operation available. Among the food pantries displaying days of operation, 78.1% were open at least once per week. Only 13.6% of food pantries were open ≤1 day per month. CONCLUSIONS: The dataset developed in this study may be linked to food access and food environment data to further examine associations between food pantries and other aspects of the consumer food system (e.g. food deserts) and population health from a systems perspective. Additional linkage with the U.S. Religion Census Data may be useful to examine associations between church communities and the spatial distribution of food pantries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".