Some Observations on the Concept and Measurement of Income Inequality
Bibliographic record
Abstract
Income inequality and redistribution have become popular subjects in both public and policy circles in the wake of concerns over apparent concentration of wealth. However, a reasonable discussion of this subject is often hampered by a lack of a clear conceptual framework and relevant facts.! First, income inequality is a relative concept that can only be measured relatively by statistical tools like the Gini coefficient; used alone, these do not provide context for the results. Second, there is no single agreed-upon goal for income redistribution; different approaches invariably involve value judgments based on ethical or political theories that can differ widely on the crucial questions of why and how much redistribution should be sought. Third, the importance of this issue requires that measurements of the scale and absolute amount of existing income redistribution be utilized to inform the discussion. This communiqué takes a sober look at facts relating to income inequality and redistribution in Canada and applies methodology to reveal that, while the scale of income redistribution has declined since 1994, growth in real income since then has done much to compensate in maintaining! levels of absolute income redistribution that are high by historical standards.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".