Spatial distribution and socioeconomic differences between urban farms' production and distribution points in Chicago, IL
Bibliographic record
Abstract
Urban farming remains popular as a potential sustainable replacement or supplement for traditional agricultural models, but little comprehensive research has been done regarding the socioeconomic characteristics of this food production and distribution model. Using the City of Chicago as a case study, this research seeks to understand where urban commercial farms are located and whether there exists a significant disparity between the social demographics of the neighbourhoods where these farms produce crops compared to where their goods are sold. The distribution of urban farm and sale points was determined by geocoding the location of all production (farms) and distribution points (sale points) for commercial urban farming companies in Chicago, then calculating spatial statistics and calculating the mean centers, standard distance, and standard deviational ellipses (SDE) for each. These were then overlaid onto choropleth maps containing socioeconomic indicator data derived from the US 2016 census. These socioeconomic indicators — median annual household income, mean home value, and percent racialized minority population — were analysed to determine if a correlation exists between each socioeconomic indicator and the location of farm and sale points. Findings reveal statistically-significant differences in the socioeconomic indicators of census tracts of farm versus sale point locations, showing a skewness in distribution of farm locations towards areas of lower socioeconomic status versus a skewness in distribution of sale point locations towards areas of higher socioeconomic status. The results suggest that, while farms are more likely to be located in marginalized neighbourhoods in Chicago, most produce grown by these farms is sold in more privileged areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".