Maluku Economic Recovery During the Covid-19 Pandemic and Entering the New Normal Era
Bibliographic record
Abstract
Covid-19 globally has affected all sectors, and the lock down has aggravated world economic conditions, including Maluku Province. The research objectives are (1) to describe the sectors affected by the Covid-19 pandemic in terms of economic growth; (2) Describe the impact of covid-19 on the workforce; (3) Knowing the policies carried out by local governments in handling the socio-economic impacts of Covid-19; (4) Formulating a strategy for economic recovery from the impact of covid-19 and entering the new normal era. The method used in this research is descriptive quantitative, from the data published by competent government agencies. The results showed (1) out of 17 sectors, there were 13 sectors that experienced a contraction in economic growth, showing that the initial impact of Covid-19 in Jakarta had contributed to the economic contraction in Maluku in tw 1 and tw-2, economic growth declined due to PSBB in the city. Ambon. (2) the number of unemployed increases with the number of layoffs and workers who are laid off. (3) Regional government policies are very maximal in terminating the chain of transmission but have not been able to reduce the level of spread, the longer efforts to deal with the transmission chain through PSBB will have an increasingly impact on the economy and (4) Economic recovery strategies can be implemented through five activities in 2020 in the third quarter to to IV and in the year 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".