JOB LOSS INSURANCE PROGRAM APPLICABLE IN INDONESIA BASED ON GOVERNMENT REGULATION AND ITS COMPARISON WITH OTHER COUNTRIES
Bibliographic record
Abstract
One of the regulations in the field of employment issued by the Government of Indonesia is Government Regulation No. 37 of 2021 concerning the Implementation of the Job Loss Insurance Program. This regulation is very important in the midst of rampant layoffs by companies as a result of the Covid-19 pandemic. The purpose of this study was to determine the implementation of a job loss insurance program for employees affected by termination of employment. The research method uses a normative juridical approach with secondary data. Based on the research results obtained that Comparison job loss insurance program in Indonesia with other countries lies in the coverage, requirements and premium contributions. Job loss guarantee or unemployment insurance in Indonesia and other countries have the same goal, which is to financially support individuals who do not have jobs as long as the individual is looking for work. This policy has been carried out since 1935 and 1940 by the United States and Canada against the background of the Great Depression around 1930, then Thailand began to implement an unemployment insurance scheme in 2004. In 2016, there were 73 (seventy-three) countries that had implemented the unemployment insurance scheme. Each country creates an unemployment insurance policy with a different model but the goal remains the same, which is to achieve adequate protection against the risk of job loss by expanding coverage to ensure workers affected by layoffs receive compensation. Based on a comparison with several other countries, it was found that the job loss insurance scheme was effective in overcoming the number of unemployed, especially during an economic recession and was able to protect workers who were laid off by maintaining their level of welfare.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".