Bibliographic record
Abstract
The COVID-19 pandemic, lockdown, and self-isolation have changed many things around the world and the same is found happening in Russia and Uzbekistan too. The article examines the impact of COVID-19 on society with an emphasis on the education sector and media of both countries. The study argues that COVID-19 disrupted the education system in schools, colleges, and universities, and the educational institutions were closed in an attempt to contain the spread of the virus. Schools were forced to replace the compulsory face-to-face in-class education with online learning and home schooling helped by both teachers and parents. The study reveals that students and teachers adapted to the online education system and were obliged to follow distance learning. However, there have been challenges adjusting to these changes for students, teachers, and parents. Also, the pressure on the medical infrastructure increased considerably. The health sector was finding it difficult to manage both professionals and the administrative aspects of handling the overall load on the medical system. Conceivably, the initial reluctance of the administration to recognize the possible enormity of the threat is probably responsible for their medical system’s failure in some parts of Russia and Uzbekistan during the pandemic. Owing to the massive influx of patients, and the inadequacies of social support, the situation deteriorated and healthcare sectors were found to be increasingly overwhelmed. The COVID-19 pandemic has taught both nations that access to healthcare and medication is of utmost importance for the survival of the state itself. The COVID-19 pandemic taught that there is a necessity to change the lifestyle and the overall teaching and learning process and upgrade the technology.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".