Economic Scar Tissue of COVID-19 Puzzle: An Analysis, Evidence and Suggestion on Global Perspective
Bibliographic record
Abstract
The COVID-19 pandemic has instigated tremendous human and economic hardship around the world. Using meta-literature and time series analysis, we conduct both synthesis and empirical analysis to investigate particularly the economic perspectives of COVID-19 across several financial systems: (a) Asian market, (b) European market, (c) American market and (d) Gulf Cooperation Council (GCC) and Middle East and North Africa’s (MENA) market. The critical review of the leading business and finance journals of ISI-WOS summarizes that the outburst of COVID-19 mercilessly affects global economies; however, the end phase of the systematic cascading effect has not clearly folded yet. The probable reasons of economic downturn are productivity reduction, labour immobility, undue job loss, scarcity of employment opportunities, discontinuation of supply chain, declining foreign exports, investment uncertainty, adverse clientele effect, etc. However, after analysing the pre- and during COVID effect on foreign reserve and remittance, we identify an inconclusive finding: (a) bullish trend, (e.g., the USA, Canada, Mexico, Japan, India, Bangladesh and Singapore); (b) bearish trend, (e.g., the UK, Sri-Lanka, Saudi Arabia, Malaysia, Nigeria, Italy and Brazil). Our time series analysis between pre- and during COVID-19 also documents the economic mystery that although the overall economic growth has gone down, foreign reserve and remittance have increased gradually across several economies. Overall, the current global situation demands systematic, well-targeted and aggressive fiscal-monetary stimulus initiatives. Therefore, this study offers theoretical, empirical and policy-oriented academic novelty with the possible suggestions and dynamic strategies to circumvent COVID-19 adverse effects.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".