Neighborhood matter: Variation in food insecurity not explained by household characteristics
Bibliographic record
Abstract
This study investigated the relationship between the social and economic contexts of neighborhoods and household food insecurity. Four years of data (2007 through 2010) drawn from the nationally representative Canadian Community Health Survey, which measures food insecurity using the Household Food Security Survey Module, were matched with the 2006 Census. Economic and social indicators from Census tracts were aggregated to the level of provincial and territorial health regions. Using random intercept logistic multi-level modeling in a Bayesian environment, with household characteristics and health region characteristics as level 1 and level 2, respectively, it was found that 14% of the variations in food insecurity prevalence lies between neighborhoods. After controlling for relevant household-level predictors, the prevalence of female-lone parent led households in a neighborhood raised the population prevalence of food insecurity by 2% as did low average household income. Therefore, the social and economic contexts in which households reside contribute independently to increased food insecurity among their residents. They reveal important differences in quality of life across Canadian provinces and territories.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".