Covid-19 and its global implications: A social policy perspective
Bibliographic record
Abstract
Covid-19 is still not a history. From 31st December 2019, the unfateful day when the outbreak of Covid-19 was declared as pneumonia in Wuhan (China) with 44 confirmed cases, to the present day of 15th December 2021, this deadly pandemic has to its credit more than 274 million reported cases and over 5 million reported deaths all across the world (WHO, 2020). Before the Covid-19, there has been a recorded history of dealing with pandemics at global level such as plague, Spanish flu (191820), Ebola (2014–16), H1N1 2009 virus and such others. These pandemics have been characterised with their high rate of mortality and geographic expanse.1 With Covid-19, the world witnessed a massive transmission with figures continuously swelling every time with new variants, Delta and Omicron, contributing to the continuity wave after wave. The survivors have continued to succumb to the long-term side-effects such as loss of eye sight, black fungus, memory lapses, weakened immunity, heart diseases and now also the possibility of a relapse. The article makes an attempt to examine the glob al implications of Covid-19 with special reference to the marginalized section of society and suggests the policy perspective to address such pandemics in the future.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".