Job Quality in the United States and Canada
Bibliographic record
Abstract
Abstract This chapter presents comparative empirical data on significant trends and developments in job quality in the United States and Canada. After discussing demographic, policy and institutional similarities and differences, key areas of job quality are compared, including nonstandard work arrangements, earnings quality, job polarization, labour market insecurity, work hours and overqualification and underemployment. Despite many similarities between these two liberal market economies, they exhibit a number of small differences in job quality and in the policies and institutions that produce them. There is evidence of greater levels of job polarization, earnings inequality, and long-term unemployment in the United States. A greater proportion of Canadian workers are in nonstandard employment arrangements, though there is greater earnings equality, labour market security and social protection in Canada. Distinct patterns of job quality connect to country differences in labour market, demographic and welfare institutions. For example, unions are more powerful in Canada as is the generosity of unemployment insurance. Despite these differences, flexible labour markets and weak regulation contribute to the rise in precarious work in both countries, pointing to the need for wage insurance, more generous unemployment insurance assistance, and more attention to active labour market policies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".