Socio-Legal Perspective of Gender Justice in Covid-19 Handling Policy in Indonesia
Bibliographic record
Abstract
The COVID-19 pandemic is not only a global health emergency but also leads to a recession of the global economy on a large scale. This economic recession will certainly also affect women and men differently. In the handling of the COVID-19 pandemic, one of the policies of Indonesia’s Government was the issuance of Government Regulation 21 of 2020 on large-scale social restrictions to accelerate the handling of COVID-19. This policy raises a wide range of impacts on women in the context of women's positions in the family and as a working woman. Thus, gender mainstreaming in the handling of COVID-19 to realize gender justice should be a special concern, especially since women have been exposed to the vulnerability of COVID-19, as well as enduring the distinctive impact of the COVID-19 handling policy regarding social roles in families and communities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".