Culture and Economic Development in Late Comers: Comparing China and India
Bibliographic record
Abstract
China and India are both late comers to industrialization. Both adopted similar economic development strategies after World War II, but the per capita GDP diverged significantly in the last 40 years. While economic growth and development have many components, we explain the difference in economic performance by emphasizing the difference in state capacity in the two economies. A country’s state capacity is affected by culture and history. China established a unified language and culture two thousand years ago that enabled it to develop strong state capacity. With a strong state capacity, China made crucial investments in infrastructure and in key heavy industries and developed technological capabilities to help start and sustain growth. India, on the other hand, is a country segmented by religion, caste, and language which has hindered the development of effective state capacity, and thus complementary state investments to spur economic growth. Moreover, India has up to now relied more heavily on expansion of its service sector compared to China, which has hindered its exports, a crucial element that helped China’s economy. India’s future industrialization crucially depends on national integration and concomitant strengthening of state capacity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".