Bibliographic record
Abstract
Since 24th March, 2020 India has been under complete lockdown immediately after following a voluntary ‘Janta Curfew’ on the appeal of Hon’ble Prime Minister Mr. Narender Modi to combat Covid-19 a worldwide pandemic. It has punched the Indian economy adversely which has already been suffering with sluggish growth rate in GDP. In the era of globalization where countries across the world are interdependent to fulfill their needs, COVId-19 has affected imports and exports adversely. According to Chief Economic Advisor, KV Subramanian the GDP growth in the first quarter (April to June) is likely to range between one to two percent due to Covid-19 led crisis and subsequent lockdown. Moreover, surveys by industry bodies have revealed that businesses are grappling with tremendous uncertainty about their future. It is not a good sign for a labour intensive country like India where unemployment rate is already higher. Keeping in view these facts the present paper has been presented to discuss the impact of Covid-19 on Indian economy and to suggest remedial measures thereof. The study is secondary data based and the same has been collected from different published reports, newspapers/magazines and internet.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".