e-brief Boomer Bulge: Dealing with the Stress of Demographic Change on Government Budgets in Canada
Bibliographic record
Abstract
While the sagging economy is focusing attention on fiscal policy’s capacity to fight a slump, another challenge looms – the demographic pressures on future program spending.1 Short-term stimulus can only succeed if it preserves confidence in the long-run capacity of Canadian governments to provide programs and service their obligations at tolerable tax rates. Notwithstanding reasonable budget balances going into the crisis, several measures show that governments are poorly prepared for the challenges ahead. The accumulated net debt in most public accounts shows potential saving already turned into consumption. Inadequately funded government-worker pensions and unfunded obligations of the Canada and Quebec Pension Plans use a slightly different language to tell a similar story. Potentially most important of all are the tabs governments face for age-related program spending in the future. These are implicit promises of services and transfer payments as the population ages that Canadians appear to be counting on, but have made no provision to pay for. Demographic changes will strain age-sensitive public programs – healthcare, education, elderly and children’s benefits – in Canada. While the responses to that strain are not yet known, we can anticipate their size by seeing what current patterns of age-sensitive spending imply for future tax rates. This e-brief assesses those current patterns of spending per person, and projects the shares of Canadian and provincial/territorial gross domestic product (GDP) they will require in the future. Falling numbers of young people will reduce the claim of education and family programs on the economy far less than rising numbers of older people will increase the claim of healthcare. Discounted over 50 years, the net increase I N
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".