MOESM1 of Homeownership status and risk of food insecurity: examining the role of housing debt, housing expenditure and housing asset using a cross-sectional population-based survey of Canadian households
Bibliographic record
Abstract
Additional file 1: Table S1. Correlation matrix between total housing expenditure and its individual expenditure components among mortgage-free homeowners. Table S2. Correlation matrix between total housing expenditure and its individual expenditure components among homeowners with a mortgage. Table S3. Correlation matrix between total housing expenditure and its individual expenditure components among market renters. Table S4. Odds ratios of household food insecurity by homeownership status among households of all incomes (n = 10,815) - reference category set to owners with a mortgage. Table S5. Odds ratios of household food insecurity by homeownership status among lower-income households (n = 5547) - reference category set to owners with a mortgage. Table S6. Odds ratios of household food insecurity by homeownership status and housing asset level among homeowners of all incomes (n = 8360). Table S7. Odds ratios of household food insecurity by homeownership status and housing asset level among homeowners with lower incomes (n = 3690). Table S8. Geographical distribution of households with different homeownership status and housing asset level. Fig. S1. Prevalence of household food insecurity by deciles of estimated home value among homeowners
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.004 |
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".