Bibliographic record
Abstract
The evolution of growth theories from the 1956 seminal work of Solow and Swan to Aghion and Howitt’s 1992 Schumpeterian model is traced herein. How growth empirics helped improve some existing theories is also presented. As a matter of fact, the empirical evidence that countries were not converging as the Solow-Swan model predicted led to the development of endogenous growth theories pioneered by Romer (1986) and Lucas (1988). Thereafter, semi-endogenous growth models originated from the observation that growth rate across countries was not proportional to the size of skilled labor as endogenous growth theories predicted. I also present my own empirical assessment of some predictions from growth theories and find supporting evidence of (1) convergence of GDP across Canada and the countries of the West African Economic and Monetary Union and (2) a positive relationship between output and the accumulation of knowledge through R&D across Canada. I also find, in Canada, the evidence of a positive relationship between economic growth and skilled labor, as some model predicted.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".