Characterizing Saskatoon’s Food Environment: A Neighbourhood-level Analysis of In-store Fruit and Vegetable Access
Bibliographic record
Abstract
This paper evaluates the relationship between in-store food offerings and neighbourhood level socio-economic and demographic characteristics in Saskatoon, Saskatchewan, as well as to assess differences in fruit and vegetable access among grocery stores in neighbourhoods of varying socioeconomic status.This study compares measures of the food environment using data based on structured observations, self-reported data and measured data. A census of 116 food stores were measured in Saskatoon’s residential neighbourhoods (n=60), of which 24 were grocery stores. Neighbourhoods were assigned to categories of high, mid and low socioeconomic status (SES) based on the Material and Social Deprivation Index. Proportion of Aboriginal ancestry by neighbourhood was also incorporated into the analysis. High SES neighbourhoods had a higher proportion of grocery stores, of all store types, than mid or low SESneighbourhoods, while low SES neighbourhoods had a much higher proportion of convenience stores compared to mid and high SES neighbourhoods. Overall in-store grocery measures did not vary signifi cantly across neighbourhood-level SES, but did vary by proportion of Aboriginal ancestry. Price and availability of fruits and vegetables varied in low SES neighbourhoods and those with a higher proportion of Aboriginal ancestry. Th is study uncovers a disproportionately high distribution of convenience stores in lower SES neighbourhoods, suggesting that food swamps are prevalent in Saskatoon and confi rms previous research findings of inequities experienced by Aboriginal people in the city. Further research, including more qualitatively-driven data, is necessary to elucidate the complexities of Saskatoon’s food environment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".