Accounting for Growth: Comparing
Bibliographic record
Abstract
T hrough most of the twentieth century, only those in the high-incomeindustrial countries, less than one-fifth of the world’s population, haveenjoyed the fruits of economic well-being. However, since 1980, China and India have achieved remarkable rates of economic growth and poverty reduction— and taken together, these countries comprise over a third of the world’s popula-tion. The emergence of China and India as major forces in the global economy has been one of the most significant economic developments of the past quarter century. This paper examines sources of economic growth in the two countries, com-paring and contrasting their experiences over the past 25 years. In many respects, China and India seem similar. Both are large geographically and have enormous populations that remain very poor. In 1980, both had extremely low per capita incomes. The World Bank and the Penn World Tables show GDP per capita for India was roughly equal to the World Bank’s 1980 average for all low-income countries, while per capita GDP for China was about two-thirds of the estimate for
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".