Innovation Management for Inclusive Growth in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The world is advancing towards globalization of markets and homogenization of products and services. The 21st Century and the new millennium have ushered in high hopes and immense opportunities and also came with its bowlful of problems. India too experienced vigorous growth with strong macro-economic fundamentals with sharp increase in savings and investment rate. Despite all the problems and struggles on the political and economic front, the Indian Economy has recorded an impressive growth rate of 5.7 per cent per annum on an average for more than 2 decades. In the post reform period, the economy has shown a secular growth path of more than 6 per cent. The economy was estimated to grow by 8.5 per cent, though toned down more due to extraneous than fundamental factors to 7.7 per cent is still good enough. Industry and service sector maintained their vigorous growth performance, particularly in manufacturing sector which recorded growth of 11per cent. India has the potential to deliver the fastest growth over the next 50 years. Those fifty golden years are not over yet. There is yet the growth story alive and we can be pursued. However, we cannot, at the same time forget the 220 million people left out of the train, farmers committing suicide, a quarter of the population still illiterate, urban poverty and infrastructural problems.
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.
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 | 0.001 |
| 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 it