Bibliographic record
Abstract
This paper produces an estimate of market-based human capital investment and stock for Canada over the period from 1970 to 2007 based on the lifetime income approach and compares it with that of physical and natural capital investment and stock. It adopts the methodology developed by Jorgenson and Fraumeni, and estimates human capital stock as the expected future lifetime income of all individuals. Human capital investment is estimated as changes in human capital stock due to the addition of new members of the working age population arising from the rearing and education of children and the effect of immigration on human capital. The main findings are as follows: 1. The volume of aggregate human capital rose at an annual rate of 1.7% in Canada for the period 1970 to 2007, and most of the growth is due to the increase in the number of individuals in the working-age population. The rising education level of the Canadian population is also a significant contributing factor to the growth in human capital. 2. The compositional effects of aging of the Canadian population (a movement to a population that is older on average) reduced human capital growth by 0.6% per year over the period 1980 to 2007, while the rising education level increased human capital growth by 0.7% per year over the period. 3. Human capital stock on a per capita basis increased at 0.9% per year for the period 1970 to 1980, due to the rising education attainment during the period. After 1980, human capital stock per capita was virtually unchanged due to two offsetting factors: rising education level which increased human capital stock and the compositional effects of population aging, which reduced human capital stock. 4. The value of human capital investment and stock exceeds the value of physical capital investment and stock, and the ratio of human capital investment and stock to physical capital investment and stock declined over time. In 2007, human capital stock is about four times
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".