Canada’s Demographic Future: Some Reflections on Projection Assumptions. Canada Pension Plan Seminar on Demographic and Economic Perspectives of Canada: Years 2000 to 2050. Ottawa: Office of the Chief Actuary
Bibliographic record
Abstract
Projections since the start of the Canada Pension Plan have been reasonably accurate with regard to population size, but they have under-projected population aging, and thus the ratio of beneficiaries to contributors. A case is made for long terms assumptions including fertility of 1.6 births per woman, life expectancy of 85, and net immigration of 0.47 per 100 population. By 2026, there will be fewer than three persons aged 20-64 per person aged 65 and over, compared to over six when the program was started. According to medium projections, the proportion 20-64 to 65+ will change from 4.9 in 2000 to 2.2 in 2100. This will also be in the context of a slower growing and aging labour force. Population policies are considered that would seek to avoid population decline and reduce the pace of aging. Demographics take on a new meaning in a welfare state, as there is interest to determine the relative well-being of various components of the population, and to plan for improvements. Population projections become more than an academic exercise. For most activities of the welfare state, like health, education and social security, short term projections are sufficient, but for pensions a long term perspective becomes more important, particularly for having a sense of the change in relative numbers
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".