Bibliographic record
Abstract
Covid-19 has affected Chinese economy greatly.Each local industries and companies are influenced by the isolation policy, and people are losing jobs.Because most industries and companies are affected, the GDP growth rate of the first quarter in 2020 have decreased a lot.After discussing how does the quarter GDP growth rate decrease detailed, the theory that coronavirus influenced Chinese economies significantly, will be proved by IS-LM model through accurate data from National Bureau of Statistics and other statistics by using formula equations.Those statistics will show the change of each element of the financial market and goods market, which are corresponding to each variable in the formula of IS-LM model.The changes in these variables give the expectation of how economy changes over time.According to the analysis in this paper, coronavirus has brought huge impact on the net export, the investment inboard and aboard, and the employment as well as salaries, leading to an overall decrease in output and the nominal interest rate.Therefore, both the goods markets and the financial markets are affected through the pandemic, corresponding to the situation in China at the first quarter of 2020.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".