PENGARUH WABAH COVID-19 TERHADAP TINGKAT PENGANGGURAN TERBUKA PADA SEKTOR TERDAMPAK DI INDONESIA
Bibliographic record
Abstract
Covid-19 pandemic which took place since the beginning of the year has hit the economy, including in Indonesia. The business sectors, especially tourism and manufacturing are the most affected. The result is the termination of employment (layoffs) or laying off workers for a while. Based on data from the Ministry of Manpower and BPJS Employment, there are 2.8 million workers directly affected by Covid-19. They consist of 1.7 million formal workers laying off and 749.4 thousand laid off. In addition, there were 282 informal workers whose businesses were disrupted. While the Badan Perlindungan Pekerja Migran Indonesia (BP2MI) recorded that there were 100,094 Pekerja Migran Indonesia (PMI) from 83 countries returning to Indonesia in the last three months. CORE Indonesia estimates that the open unemployment rate in the second quarter of 2020 will reach 8.2% with a mild scenario. While other scenarios were 9.79% in the medium scenario and 11.47% were severe scenarios. The Indonesian Monetary Fund (IMF) also projects Indonesia's unemployment rate in 2020 to be 7.5%, increasing from 2019 which is only 5.3%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".