Disability Considerations for Measuring Poverty in Canada Using the Market Basket Measure
Bibliographic record
Abstract
Abstract Persons with disability (PWD) in Canada experience disproportionately high poverty rates. Poverty measures are often used to benchmark income assistance levels and social policies across Canada. The Market Basket Measure (MBM) is the official poverty measure in Canada that accounts for differences in family composition and geography. It does not, however, account for cost-incurring factors like disabilities, despite the evidence of differences in daily living costs. PWD experiencing poverty have additional needs to reduce barriers to full participation in society that can translate to higher basic costs for daily living. Given that poverty measures like the MBM may assess eligibility for income support or eligibility for public housing, these measures need to reflect how the cost of living differs for PWD. To critically analyze disability-specific considerations for the Canadian poverty line, we assess the MBM within the context of persons with disabilities. To identify differences in consumption patterns and family composition for PWD, a population based cross-sectional analysis was conducted using data from the 2017 Canadian Survey on Disability. Analysis assessed for bias within the MBM based on the basket contents, family composition and disability severity. PWD experience two times higher poverty rates, worse housing outcomes and incur higher and additional expenses for basic needs of daily living than persons without disability. The MBM underestimates the true poverty rate for persons with disabilities as it does not account for all their additional costs and does not represent their average family composition.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".