Economic Growth in <scp>C</scp>anada and the <scp>U</scp>nited <scp>S</scp>tates: Supply‐Push or Demand‐Pull?
Why this work is in the frame
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Bibliographic record
Abstract
Understanding the nature of structural change and the sources of economic growth of an economy, especially the relative importance of different industries, is essential for policy‐making. This paper estimates industry contribution to economic growth in both C anada and the U nited S tates. It argues that industry contribution should be evaluated on the basis of the performance of an industry in terms of creating economic value relative to other industries. In particular, it calls for the quantity and the price effects, which is consistent with real GDP in the chained‐Fisher index that values the industry more when its price rises and less when its price declines. This is an important departure from the traditional methodologies that consider only quantity effect. This paper shows that the contribution from demand‐driven industries is significantly more than the finding based on traditional thinking.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it