Bibliographic record
Abstract
The differential multiple tax regime across sectors of production leads to distortions in allocation of resources thus introducing inefficiencies in the sectors of domestic production. Efficient allocation of productive resources and providing full tax offsets is expected to result in gains for GDP. In sum, implementation of a comprehensive GST in India is expected to lead to efficient allocation of factors of production thus leading to gains in GDP. Terming the first quarter GDP data as a matter of concern, the govt. Requires both in policy and investment to work to improve the figure. A detailed analysis shows that while agriculture is in the normal range, manufacturing has bottomed out to 1.6 percent from 3.1 percent. The economic survey had projected a growth of 6.75 per cent to 7.5 per cent for 2017-18. The Indian economy expanded 5.7 percent year-on-year in the second quarter of 2017, below 6.1 percent in the previous period and market expectations of 6.6 percent. It remains the weakest growth rate since the first quarter of 2014 due to a slowdown in consumer spending and exports. On the production side, manufacturing and agriculture eased. GDP Annual Growth Rate in India averaged 6.12 percent from 1951 until 2017, reaching an all time high of 11.40 percent in the first quarter of 2010 and a record low of -5.20 percent in the fourth quarter of 1979. Economic growth plunged to 5.7 per cent in April- June of the current financial year 2017-18 due to destocking by companies following pre GST fears. Growth in manufacturing declined to 1.2 per cent in April-June from 5.3 per cent in January-March.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".