Bibliographic record
Abstract
Abstract This article first takes stock of what we know about the patterns observed in Indian development and speculates on the likely scenarios over the next few decades. The main question that the article concerns—and probably the biggest economic policy challenge today—is why has poverty in India declined so slowly? The article suggests that a proximate cause is the size and productivity levels of the informal sector—a bulk of India's labor force is engaged in low-productivity cottage-type activities with little physical or human capital. This hinders productivity. What then is responsible for the existence and continuation of constraints in the informal sector? If the poor incidence of entrepreneurship in the informal sector is because of poor infrastructure and weak financial inclusion, why have governance structures failed to alleviate these constraints? In addition, the formal sector has not expanded at the expense of the informal sector to absorb a greater part of the labor force. The article discusses a host of factors that could explain obstacles to productivity improvements in the informal sector, and hence the low growth–poverty elasticity: caste, collective action, the political economy of Indian democracy, the role of credit markets, and rural urban migration, among others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".