Bibliographic record
Abstract
This paper investigates the relationship between real per capita public health care expenditures and age distribution in Canada and the United States while controlling for other important factors. This study uses Province-level data for the period of 1981–2004 for Canada and state-level data for the time period of 1991-2004 are used for the USA. The main objective of the paper is to find a difference on Public Health Care expenditures between Canada and the US amongst the older age groups of the population (65 and over). Using DOLS (dynamic ordinarily least squares) and first-difference regressions for Canada and the US respectively, we find differences for several of the age groups. The most interesting result is that the age group of 65-69 yields a non-significant impact in Canada, but in the USA it yields a positive and significant effect on public health care expenditures. This is an important result because of the aging North-American population; it undoubtedly reflects the need for adequate health care policies in both countries,
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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) | 0.001 | 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".