RESILIENCY OF WOMEN'S LAYOUT OF IMPACT OF THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Reflecting on the implementation of physical distancing in Greater Jakarta since March 2021 due to the COVID-19 pandemic, Indonesia's economy grew 2.97% lower than the target of 4.4%. On the same occasion, he said that state budget revenue for the first quarter of 2020 still recorded growth of 7.7 percent or 16.8 percent. Meanwhile, absorption of state spending grew slightly by 0.1 percent to 17.8 percent in the first quarter of 2020. (Hanoatubun, 2020) During the COVID-19 pandemic, more than 1.5 million workers were laid off and laid off. This study aims to measure the resilience of women who have been laid off by the impact of the COVID-19 pandemic in carrying out their roles in the family. The method in this study uses a qualitative research method with a case study approach with purposive sampling. The results of this study found that the research subjects were able to overcome the pressure that occurred by being patient, enthusiastic, optimistic about the efforts made, and able to be grateful for the existing conditions. They can recover well and remain productive in carrying out their daily activities. Positive support from the closest people also plays a role in building the resilience of each subject.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".