Public Assistance Programs Help Mitigate the Adverse Economic Effects of Covid-19 on Argentine Households
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.003 |
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".