Bibliographic record
Abstract
The Indian economy experienced an economic shock and a \npublic health crisis as a result of the coronavirus disease \n(COVID-19) pandemic and related restrictions from March \n2020, and entered uncharted territory as it navigated global economic uncertainty and patterns of contraction and growth \nthat marked the paths of most economies in 2020–22. In the \nlast two quarters of fiscal year 2020/21 there was a resurgence of economic growth in the country. From the depths of the June quarter, when gross domestic product (GDP) shrank by 22.4%, there was sharp recovery in the next quarter (a fall of 7.3%), followed by growth of 0.4% in the December quarter and \ngrowth of 1.6% in the final quarter of 2020/21. For the full \nfinancial year, the country beat the gloomy forecast of negative growth of 8% and recorded a contraction of 6.6% in GDP. \nHowever, this was worse than the negative growth of 4.9% \npredicted for the global economy during 2020 by the International Monetary Fund (IMF) in its June 2020 update. In the \nfourth quarter Indian gross value added (GVA) grew at 3.7% \nyear on year, after recording negative growth of 22.4% and \n7.3% in the first and second quarters, respectively, and growth \nof 1.0% in the third quarter. For the full financial year 2021/22 \nIndia’s real GDP grew by 8.7%. In 2021/22 GVA grew at 8.1% \nand net taxes on products grew by 16.1%. In its April 2022 \nWorld Economic Outlook the IMF estimates the Indian economy \nwill grow by 8.2% in 2022, compared to 3.6% for the global \neconomy, 3.3% for the advanced economies, 4.4% for the \nPeople’s Republic of China and 3.8% for emerging and developing economies. The IMF has forecast India’s GDP to grow at 6.9% in 2023, while the Reserve Bank of India (RBI) has forecast GDP growth of 7.2% in 2022/23. GDP at the end of 2021/22 had surpassed the 2019 level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.259 | 0.140 |
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".