Polarization and the Decline of the Middle Class: Canada and the US
Bibliographic record
Abstract
Several recent studies have suggested that the distribution of income (earnings, jobs) is becoming more polarized. Much of the evidence presented in support of this view consists of demonstrating that the population share in an arbitrarily chosen middle income class has fallen. However, such evidence can be criticized as being range-specific - depending on the particular cutoffs selected. In this paper we propose a range-free approach to measuring the middle class and polarization, based on partial orderings. The approach yields two polarization curves which, like the Lorenz curve in inequality analysis, signal unambiguous increases in polarization. It also leads to an intuitive new index of polarization that is shown to be closely related to the Gini coefficient. We apply the new methodology to income and earnings data from the US and Canada, and find that polarization is on the rise in the US but is stable or declining in Canada. A cross-country comparison reveals the US to be unambiguously more polarized than Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".