A comparative study of the performance of Iran and G7 countries in the management of COVID-19
Bibliographic record
Abstract
Purpose The purpose of this paper is to compare the performance of Iran and G7 countries in the management of the COVID-19 crisis. Design/methodology/approach The indicators and statistics provided by the Oxford Government Response Tracker are used in this research. Sixteen indicators and their related items have been analyzed for eight countries including Iran, Canada, Germany, France, Great Britain, Italy, Japan, and the United States. For data analysis, Multivariate analysis of variance (MANOVA) and Tukey’s post hoc test were applied, and structural equation modeling performed with the help of SPSS and Smart-PLS software. Findings The results show that 8 indicators of closing schools, cancellation of public events, restriction of gatherings, restriction of domestic travel, restriction of international travel, reduction of household debt, testing policy, and contact tracing, have an effect on the number of deaths in the countries under review. The results also showed that the countries exhibit behaviors outside their normal culture during the crisis. Originality/value This paper will be helpful for scholars, as well as policymakers when making policies on the appropriate responses to COVID-19 and similar pandemics.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".