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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".