More Unequal Yet More Alike: The Changing Anatomy of Constituent Canadian Income Distributions in the 21st Century
Bibliographic record
Abstract
The Canadian income distribution is a mixture of many very different constituent income distributions, Aboriginal–non-Aboriginal, Male–Female, Urban–Rural. In part, the extent to which they differ reflects the Inequity of Life Chances across those various constituencies, which has long been one focus of the Canadian political agenda. A core component of the equal opportunity imperative is that, conditional on circumstance and effort, each and all should have the same opportunity for income. Assuming innate efforts and abilities are commonly distributed across those constituencies, measuring the extent of "inequality of opportunity" is a matter of measuring the manner and extent to which constituent income distributions are unequal. This task is somewhat obfuscated by the counterintuitive fact (illuminated by a subgroup decomposition of the Gini coefficient) that a collection of distributions can simultaneously become more (less) equal and more (less) polarized. Thus, it is possible that constituencies can at once become less equal and yet, at the same time, have more in common. Here, in a study of the evolution of Income distributions of Aboriginal–Non-Aboriginal, Male–Female and Urban–Rural constituencies in Canada, three new tools are introduced for measuring the degree of segmentation and polarization in a collection of constituencies and the extent of ambiguity in an Income Wellbeing ordering of those constituencies. The study reveals increasing inequality coincident with diminishing segmentation and polarization in the first decade of the 21st century indicating some small advancement of the Equal Opportunity agenda.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".