Bibliographic record
Abstract
To lower poverty and to raise living standards, many developing countries need to attain and maintain fast, pro-poor growth. Arguably the most important acceleration to occur over the last half-century has been India’s. The country had suffered both low average income and slow growth over most of its first three post-Independence decades. Unlike the short and clear-cut periods of “take-off†experienced by many countries, India’s appears to have been a two or three step process beginning in the 1970s and ending with the upward ratcheting of the early 1990s. A striking feature was the small increase in the (constant price) investment rate, implying that the dominant proximate cause of acceleration was increasing efficiency in the use of resources. Possible factors at work include the shift away from the Mahalinobis model, the liberalization and improvement in business atmosphere, the Green Revolution, and the creation of many new bank branches which raised national savings. Inequality appears to have changed little during the accelerations of the late 1970s and 1980s but it rose significantly during that of the 1990s. Even then, however, the income growth of the poorer groups, including agricultural wage earners and casual non-agricultural workers, was strong.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".