Differences In Financial Performance And Earning Persistence Before And During The Covid-19 Pandemi
Bibliographic record
Abstract
The Covid-19 pandemic which has been going on since the beginning of 2020 has had an impact on changes in social life and a decline in economic performance in various countries in the world that have been affected by Covid-19. The decline in Indonesia's economic performance has occurred since the first quarter of 2020, which is reflected in the rate of economic growth in the first quarter of 2020 which only reached 2.97 percent, and again decreased significantly in the second quarter of 2020 which grew -5.32% (Central Statistics Agency, 2021a; Central Bureau of Statistics, 2021b). The results of a pandemic impact survey conducted by the Central Statistics Agency (BPS) on 34,559 business actors revealed that 82.55 percent of business actors surveyed experienced a decrease in income. This is because Covid 19 has had an impact on company productivity. However, there are several companies that claim that their income has not been affected by the pandemic, and there are even a small number of companies that claim that their income has increased during the pandemic. With conditions that are increasingly declining as described above, the company experiences profit gains with fluctuating fluctuations as a result of the process of supply and demand as well as unequal expenses and income. Economic growth declined until it was followed by an economic contraction, such a phenomenon could affect the persistence of profits and company performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".