Bibliographic record
Abstract
Abstract We assess the evolution of consumption inequality in Canada over the years 1997 to 2009. We correct the imputation of shelter consumption for homeowners to allow for unobserved differences in housing quality correlated with selection into rental tenure, and we account for measurement error in this imputation. Using the annual Survey of Household Spending for years 1997 to 2009, we find that household‐level consumption inequality measured by the Gini coefficient increased from 0.251 to 0.275 over 1997 to 2006. Between 2006 and 2007, consumption inequality may have decreased, and over 2007 to 2009, consumption inequality was flat. Over the entire period of 1997 to 2009, consumption inequality increased moderately. The Gini coefficient for individual‐level consumption inequality followed a similar pattern, though the changes were smaller in magnitude. We also explore a possible correction for tail non‐response bias in inequality measurement and find that the increase in measured consumption inequality is robust to this correction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".