Exploring the impacts of Covid-19 on the electronic product trade of the G-7 countries: A complex network analysis approach and panel data analysis
Bibliographic record
Abstract
The present study attempts to explore the impacts of COVID-19 on the intra-group electronic product trade of the world's seven largest economies. In line with this purpose, we performed a complex network analysis of the electronic product trade of the group of seven (G-7) countries and China, as well as a panel data study comprising solely the G-7 countries. In this regard, we investigated the trade networks within the G-7 countries, to which China has been added, and determined the prominent countries in the network during the pandemic to be China, the USA and Canada. The findings also revealed that China, one of the pioneering countries in electronic product trade, has the most ties in electronic products exports with the USA, the other countries with which the USA had the most ties were Japan and Germany, apart from Canada. It was discovered that Germany was the most active country in the network, following the USA, in terms of export ties and the number of export countries in its network. The panel data analysis, on the other hand, yielded two different models, namely import and export, based on 22 months of data, from March 2020 to December 2021, considering the World Health Organization's (WHO) declaration of COVID-19 as a pandemic on March 11, 2020. The findings showed that independent variables affecting the electronic product trade within G-7 countries bore different effects in both models, that the deaths/cases ratio, the tests/cases ratio and the number of cases had adverse impacts while the population had positive impacts on exports in the first model, and that the tests/population ratio had adverse effects while the number of tests and the population had positive impacts on intra-group electronic product imports.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".