Economic Slow Down: An Empirical Study of Indian Core Industries Performance
Bibliographic record
Abstract
Reflecting a slowdown in the economy, the growth rate of eight core infrastructure sectors dipped to 2% in March and 4.3% during 2011-12 on account of poor performance in crude oil and natural gas. The growth rate of eight industries like crude oil, petroleum refinery products, natural gas, fertilisers, coal, electricity, cement and finished steel etc. have a weightage of 37.9% in the Index of Industrial Production (IIP), in March 2012 moderated to 2% from 6.5% in the same month last year. India's GDP growth slows down to 6.1% in the third quarter of 2011-2 over the corresponding quarter of the previous year the lowest in 2 years. The impact of economic slowdown has been felt in all section of industry including agriculture and service also. The figure makes central statistical organization's (CSO) forecast of 6.9% growth in the financial year ending march 2012 look optimist given the slippages in agriculture and manufacturing sectors, it will be difficult for GDP to recover much ground in January-March period the undefined GDP during the 1st 9 month of 2011-12 has grown to 6.9% only way below 8.1% growth recorded during the same period a year ago. This empirical study highlights the performance of Indian core industries during the economic slowdown. Secondary sources have used to analyse the paper.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".