Public Assistance Programs Help Mitigate the Adverse Economic Effects of Covid-19 on Argentine Households
Why this work is in the frame
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Bibliographic record
Abstract
The Covid-19 pandemic caused a significant \neconomic crisis in Argentina. GDP fell by 19% in the \nsecond quarter of 2020, while unemployment rates \nsoared. As a result, 1.5 million people have fallen \ninto poverty. \n \nParticularly affected were informal workers and \nthose employed in the tourism sector. In addition, \nthe pandemic's impact on the labor market \ndisproportionately affected women. \n \nThe gender-based difference was particularly stark among young adults (18-24): while the employment \nrate for men fell by 63%, it fell by 80% for women. \nMoreover, young adult women with children are also \nat high risk of unemployment. Employment rates for \nyoung fathers have declined by nearly 57%, while \nthose for young mothers have plummeted by 82%. \n \nWith Argentina's worsening socio-economic \nconditions, it is critical to understand what policy \noptions most effectively mitigate the pandemic's \nadverse effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it