Bibliographic record
Abstract
Using the 1994 Canadian General Social Survey, this study examines the economic effects of retiring because of poor health. When men and women who have retired for reasons of poor health are compared to those who have retired for other reasons, the health retirees are disadvantaged on measures of their health, on human capital variables, in terms of their work history, and ultimately, in their retirement income whether personal or household. The men who retired because of ill health were less likely to receive income from a private pension or from interest and dividends. Almost half of the men reported that their financial situation was worse since their retirement. The women retirees suffered from the same disadvantages as the men although their incomes in retirement were much lower. In the multivariate analyses, health had a significant and negative effect on men's household and personal incomes but there was no effect on the incomes of women. For them, any effect that poor health might have had on household income was offset by factors associated with marriage, and the women's own socio-demographic characteristics. The findings suggest reason for policy-makers to be cautious when contemplating blanket reductions in disability/invalidity and pension rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".