Measuring Poverty and Material Deprivation
Bibliographic record
Abstract
Should Statistics Canada add a set of questions on material deprivation — inadequate food, housing, medical care, and other basic needs — to its income surveys? There are two main arguments in favor of doing so. One is that such information about living standards is intrinsically interesting. The other is that such information is needed in order to accurately measure poverty; it is a necessary complement to data on income and other financial assets. A consistent finding from research on material deprivation since the late 1970s is that there is only moderate overlap between households with low (single-year) income and those that lack various basic material goods and services. Contemporary interest in indicators of material deprivation was initiated by Peter Townsend (1979) in the United Kingdom and by Susan Mayer and Christopher Jencks (1989) in the United States. Their studies led to formal and regularized data collection on material hardship in these two countries, via the "Breadline
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".