Finding food deserts : a spatial analysis of food security in Northwestern Ontario (1996-2006) / by Sarah Wintle.
Bibliographic record
Abstract
The purpose of this study is to examine the state of food security in Northwestern Ontario communities by identifying food deserts, or neighbourhoods with high levels of social deprivation and limited access to nutritious food. This objective was achieved through mapping out socio demographic and economic data with the location of food retail outlets. Using a quartile analysis, many socio-demographic factors shown in the literature to affect community food security were combined into one value to be shown on one map. The resulting food desert maps were created for the years 1996, 2001 and 2006 in order to determine whether temporal trends of increasing or decreasing food security could be observed. Results show that in most communities food desert propagation follows socio-demographic and economic trends and food security was \ntherefore inferred to be improving with a marked decrease in food deserts overall. Thunder Bay was found to be an interesting combination of the factors that have contributed to food desert proliferation in the United States with the social facets that have kept them from becoming too drastic a problem in Canada thus far. To date, these are believed to be the smallest communities examined for food desert identification.
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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 | 0.002 | 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) | 0.004 | 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".