School Enrolment Is Down; Spending Is Up. What’s Wrong With This Picture? C.D. Howe Institute e-brief
Bibliographic record
Abstract
The aging of Canada’s population in the coming decades will drive up the cost of many government programs, such as public health insurance and elderly benefits. In the area of primary and secondary education, though, the opposite should be true (Robson 2003). With a declining share of youngsters in the population, governments should be able to spend relatively less on their education and redirect resources to other programs. That reallocation of public funds should already be under way because in most provinces, kindergarten-to-grade 12 (K-12) enrolments have been falling significantly since the mid-1990s. With no corresponding decline in spending, however, falling enrolments have not freed up any margin in provincial budgets. One cause of the phenomenon is that there are no mechanism to ensure that overall education budgets respond closely to changes in enrolment. Some budget envelopes are set as nominal global amounts — whatever was spent last year plus a given amount or percentage increase — amounts that are politically popular, while not reflecting variations in real funding requirements. For other parts of the
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.059 | 0.019 |
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".