Is India Really Competing with China on a Macro Economic Base
Bibliographic record
Abstract
Macroeconomics has been aptly defined as “the study of the major economic ‘totals’ or aggregates-- total production (GNP), total employment and unemployment, the average price level of all goods and services, the total money supply, and others” (Gordon, 1978). The word macro is derived from the Greek word macros meaning large The economy of India has seen rapid growth in recent years. Spurred by good domestic demand, steady and significant reforms, lower crude prices and increased skilled labor India is the fastest growing economy among the BRICS nations. Another Asian country which is also in the fast lane with respect to macroeconomic growth is China. China’s growth has primarily been attributed to a sharp sustained increase in productivity accompanied by increase in capital accumulation, improved infrastructure and cheap labor force (Hu & Khan, 1997). Both India and China have emerged as significant forces in the global economy over the last two decades. Both countries are geographically very large and have a huge population. Both countries have also achieved remarkable rates of economic growth and poverty reduction since 1980,with India doubling its per capita GDP and China posting a seven fold increase in its per capita GDP (Bosworth & Collins, 2008) .A PWC report titled “World in 2050” predicts China and India to be world leading powerhouse economies by 2050.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".