Bibliographic record
Abstract
This paper reports the effects of coronavirus on Jordan's economic growth by using quarterly data for the period (2018/2019 Q1 – 2019/2020 Q4), where the numbers of people who are sick with coronavirus and those that have died from the virus are explanatory variables, and economic growth is an affected variable. The research concentrates on analyzing reviews of theoretical and empirical literature to show the effect of coronavirus on economic growth and explaining this effect in Jordan in this period by using the ARDL technique in Eviews. By using quarterly data for (2018/2019 Q1 – 2019/2020 Q4) at a significance level of 5%, this research demonstrates that the numbers of people who are ill with coronavirus and those that have died from the virus have a weak positive effect and a negative but significant effect on the economic growth of Jordan, respectively. The research also shows a recommendation of limiting the negative effects of coronavirus by reducing the number of deaths via strengthening the health service and opening some economic sectors to boost economic growth in the country.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".