Bibliographic record
Abstract
The Covid-19 pandemic changed the world and accelerated processes that could have taken decades without a pandemic. In this paper, the authors discuss the public and government responses to the new normal, nowadays reality, and most importantly, the legal regulations that have been enacted in different countries in response to the challenges. The paper discusses in detail issues related to security measures, social distance, gender issues, abortion, education and student mobility, employment, and entrepreneurship. A pandemic that has survived more than a year needs to be addressed. The decision-makers made efforts to create a provision for the influenza virus after it became prominent in society. The intention is not to be pessimistic but to be optimistic enough to create provisions for the future. Countries are aiming to achieve their commitments to recover from the pandemic. A pandemic demands a legal response as well as a social response. The research paper aimed to divert the attention of the readers to the untouched aspects of the law that are related to emergency situations, including pandemics. In the paper, we discuss the paradox of the pandemic, lockdown, and post-lock- down situations, as well as protests/riots, gender-based violence, healthcare, and education topics related to the changes that have taken place due to the pandemic.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".