What Is Happening to Earnings Inequality in Canada in the 1990s?
Bibliographic record
Abstract
It is now commonly accepted that earnings inequality – that is, the gap in earnings between low- and high-wage workers, became more pronounced in Canada throughout the 1980s. 1 This is in contrast to inequality trends in family income, 2 which have changed little (Beach and Slotsve, 1996). This article updates and adds to the earnings inequality story by addressing two issues: 1) the evolution of the earnings gap during the 1990s; and 2) the impact of changing patterns of job-holding on the earnings gap. With respect to the first of those issues, what has happened to the inequality of annual earnings among all Canadian paid workers – men and women combined – during the 1990s? Labour market trends have been very different for men and women, and hence many studies report separate results for them (e.g. Beach and Slotsve, 1996); at the same time, the widening earnings gap among males is noted. But much of the inequality story relates to the offsetting trends between men and women, and that aspect is lost if the focus is not on all workers. Others have observed that earnings inequality and polarization among all paid workers have increased only slowly (Wolfson, 1996b) or not at all over the late 1980s and the early 1990s (Zyblock,
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".