Migration of elderly households in Canada, 1991–1996: determinants and differences
Bibliographic record
Abstract
Abstract The elderly, already a significant component of internal migration in Canada, will be an increasingly important element of future internal migration. Using census data on households, the flows and determinants of elderly migration are examined and compared with younger groups. Few migrate as individuals, most moves are multiple‐person moves, and the household, particularly the family, is the appropriate unit of analysis. In 1996 over 88% of those who migrated in the previous five years were members of households of two or more persons. The elderly are compared with the near‐elderly and those likely to be in the labour force, using the age of the primary household maintainer, a person named by each household, to classify households. The elderly are households with a primary household maintainer 65 years of age or over, the near‐elderly have a primary household maintainer of 55 to 64 years, and independent decision‐makers, the major component of the labour force, have a primary household maintainer aged 25 to 54. Significant differences are found between the groups. The elderly have a lower rate of migration and their migration determinants and destinations are less driven by employment considerations. While education, for example, is a consistently‐found determinant of migration for those in the labour force, it is much less significant for the elderly. The human capital model of migration is not applicable to the elderly. For couples both the characteristics of the primary household maintainer and the characteristics of the spouse or common‐law partner are determinants of the couple's migration, underlining the importance of modelling migration as a household move. Single‐person households often, but not always, show results similar to those for couples. When male and female single‐person households are compared, migration determinants usually have a stronger effect on females then on males. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".