Socio-economic Impact of COVID-19 till Second Wave in India: A case study
Bibliographic record
Abstract
COVID-19 pandemic shrouded the humanity in the dark clouds of deaths, long term health issues, economic deterioration, and social crisis.Economically developed and developing countries alike faced the wrath of the pandemic severely.India being the 2 nd most populated country in the world, having a middle income developing market with sixth largest economy, was no exception.After the US, India secured its position of having the second highest number of cases and deaths across the world, throughout its two phases of pandemic spread.Lack of obedience towards the COVID-19 guidelines of social distancing and fueled by the overburdened and crippled health infrastructure led to the devastation of the second wave.People from different financial classes were affected inequitably, particularly underprivileged ones suffering from reverse-migration, unemployment and psychological distress.An analysis of the development of the pandemic, the contributing factors and the socio-economic impacts have been carried out.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".