The Impact of COVID-19 on Financial Markets and the Real Economy
Bibliographic record
Abstract
This reprint comprises 17 papers published in the Special Issue entitled “The Impact of COVID-19 on Financial Markets and the Real Economy”, centered on socioeconomic models affected by the pandemic (Vasin, 2022); the COVID-19 impact on various sectors or the economy as a whole, for instance in Canada (Singh et al., 2022), China (Habibi et al., 2022), Slovakia (Svabova et al., 2022), the United States (Rodousakis & Soklis, 2022) or Vietnam (Huynh et al., 2021; Nguyen et al., 2022); the benefits of teleworking on the continuity of operations across various sectors (Santos et al., 2022); research of the tourism and recreational possibilities of Russia and Kazakhstan’s cross-border regions and the threats these areas faced during the pandemic (Tanina et al., 2022), the impact of the new coronavirus infection on the Russian labor market (Rodionov et al., 2022); the factors driving young Vietnamese people’s intention to use financial technology in the context of the COVID-19 outbreak (Khuong et al., 2022) or those influencing access to credit for informal labor sector (Vu and Ho, 2022); predicting and analyzing Jordanian insurance firms’ performance (Altarawneh et al., 2022) or developing an early warning system for solvency risk in the banking industry (Hidayat et al., 2022) during the COVID-19 pandemic; the impact of the pandemic on European stock markets (Keliuotyte-Staniuleniene and Kviklis, 2022); the drivers of cross-border mergers and acquisitions during the pandemic (Lee et al., 2021); examining the financial and fiscal variables of Ecuadorian economic groups (Tulcanaza-Prieto and Morocho-Cayamcela, 2021).
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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 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".