Basic Living Expenses for the Canadian Elderly
Bibliographic record
Abstract
Our research undertakes to determine the basic living expenses required by Canadian seniors living in different circumstances in terms of age, gen-der, city of residence, household size, homeowner or renter, means of trans-portation and health status. The paper develops required expenses for food, shelter, health care, transportation and miscellaneous. The research identi-fies the typical expenses of seniors in each of these categories. Using 2001 as our base year, we follow the US Elder Standard to build an elderly threshold for Halifax, Montreal, Toronto, Calgary and Vancouver. The research is unique because it is the first Canadian study of abso-lute basic living expenses tailored to seniors, rather than simply to adults in general. This information is important to seniors, prospective retirees, finan-cial planners, policy makers and actuaries in assessing the minimum level of income required in retirement and the adequacy of savings and income se-curity programs. Our conclusions suggest that individual circumstances, rather than age, are the primary drivers in determining the cost of these basic expenses. Se-niors are a diverse group, particularly with respect to health, so it is impor-tant that seniors and financial planners do not blindly rely on a fixed replace-ment ratio or universal level of income when projecting the level of finances needed to retire. This research enables the reader to determine the threshold that is suited to a senior’s general circumstances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".