Changes in Wage Distributions of Wage Earners in Canada: 2000-2005
Bibliographic record
Abstract
This research attempts to figure out whether the wage distributions of Canadian wage earners have been moving towards or away from the flowing three ideals in the early part of the 21th century. First, there be a pattern of wage increase that is shared by a large majority of wage earners. Second, the historical gender inequality in wage be reduced. Third, there be a decrease in wage inequality for both males and females. We use the long-form records of the 2001 and 2006 population censuses to carry out our investigation. A nice feature of these records is that the values of income variables are not top-coded so that the true averages will not be understated and good insights into the situations of those with extremely high incomes can be obtained. We are disappointed by finding that the Canadian economy mostly drifted away from our three ideals, with the main exception being that for female wage earners the improvement in wage was fortunately shared by a large majority. We believe that an important reason for our disappointing finding is the progressive entrenchment of market fundamentalism in Canada. Incidentally, we have discovered that Statistics Canada did a good job in designing the 2006 census questionnaire so that the annoying choppiness that occurred to the 2000 wage distributions vanished in the 2005 wage distributions.
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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 | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".