Risk Sharing through Social Security Retirement Income Systems: A Comparison of Canada and the United States
Bibliographic record
Abstract
Workers bear risk through variations in compensation due to changes in wage and nonwage compensation, the hours and tenure on their job, and benefits from government labor market programs. Insights about worker risk-bearing can be gained through comparisons of the Canadian and U.S. labor markets, which is the topic of a recently published Upjohn volume (Turner 2001). Labor markets in the two countries have many similarities (such as facing the aging of the baby-boom generation) and many interconnections (they exchange more goods and services than any other two countries in the world). The social security old-age benefit programs in Canada and the United States provide social insurance that reduces risk bearing by workers and are one aspect of the pattern of risk-bearing in the two countries. Perhaps because of societal differences concerning the role of government, the Canadian and U.S. programs differ in ways that affect the amount of risk-bearing they provide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".