Intracohort Income Status Maintenance: An Analysis of the Later Life Course
Bibliographic record
Abstract
This paper examines the extent to which an individual's income status position relative to others in one's own cohort is maintained over the later life course. Changes in the income status of individuals are estimated within a synthetic cohort. Using a series of cross-sectional datafiles from about every fifth Survey of Consumer Finances starting in 1974, the findings show that individuals born between 1924 and 1928 with early life socio-economic status advantages, namely high education, improve their absolute and relative income status position vis-à-vis others in their own cohort with status disadvantages from ages 46 to 64. Over the ages of 65 to 74, the pattern of economic well-being of individuals with status advantages and disadvantages reflects an income status convergence. Because Canada's old-age public welfare state is relatively well-developed in terms of comprehensiveness and generosity, it does a good job at countering the effects of status background characteristics on the distribution of income in old age; that is, it substantially weakens the relationship between education and income as individuals enter old age. In absence of these programs (i.e. up to age 64), the relative position of those with high education and other advantaged groups is strengthened.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".