Assessment of R&D and its impact on Indian manufacturing industries
Bibliographic record
Abstract
An important source of productivity growth, technological change and hence increased welfare of a country is Research and Development (R&D). Thus, it is absolutely important to develop a country's R&D sector. However, developing countries have traditionally relied largely on import of technologies from developed countries, rather than domestic R&D for driving their technological change. India too has been no exception. But, like any other developing country, India too needs to make continuous investment either to adopt foreign technology or to develop its own capacities via R&D activities. Presently, India's R&D expenditure is merely 2.1% of total global expenditure. Against this backdrop, the present study computes elasticity of industry-level TPF with respect to R&D content of intermediates, both domestic and foreign, for industries in India. The results show that R&D stocks embodied in intermediates have contributed to productivity growth in these industries. Particularly, noteworthy is this elasticity for low-R&D industries, like, processed food, textile and wearing apparels, motor vehicles etc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".