Bibliographic record
Abstract
The article examines the negative changes in the Indian economy since the beginning of 2020 under the pressure of the COVID-19 pandemic and measures to overcome them. The increase in the number of cases, the introduction of quarantine led to a rapid reduction in production, mass unemployment, and a decrease in the country’s GDP. In the current emergency conditions, it became an objective necessity to increase the impact on the situation of the nation state in various forms. It took dramatic changes in the economic policy of India of the previous period, when the position of private entrepreneurship was significantly strengthened, especially in industry and services. The COVID-19 pandemic forced the government of the country, led by Prime Minister Narendra Modi, to tackle the primary challenge - to keep the country from sliding into total prolonged stagnation and at the same time to support a multimillion poverty-stricken population. The main method of the government was the policy of financial saturation of the economy through direct financial injections, as well as the direct distribution of money and food in kind among the poor. The collapse of economic activity in India in the first half of 2020 was replaced in the third quarter of this year by signs of some economic recovery. For India, according to UNCTAD, in 2021, the opportunity to attract significant foreign investment from the leading countries of the world, interested in expanding their positions in its huge domestic market, is increasing. In the context of the pandemic, India’s role in revitalizing, at the initiative of N. Modi, political and economic contacts in South Asia between SAARC members became especially significant.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".