AUXÍLIO EMERGENCIAL E A CRISE DA COVID-19 NO BRASIL: UMA ANÁLISE MACRORREGIONAL
Bibliographic record
Abstract
Based on a review of the literature about the emergence of Cash Transfer Programs in Latin America, this work aims to analyze the scope of the Programa Auxilio Emergencial, developed by the Brazilian government in 2020, in the context of the Covid-19 pandemic emerge.The analysis is focused on the macro-regions South, Southeast and Northeast of the country, segregating the study by the variables, race, gender, income range, condition of occupation of the household and professional occupation.The PNAD COVID-19 survey, carried out by the Institulo Brasileiro de Geografia e Estatística (IBGE), is used as a database.The information collected shows that the existence of social programs prior to the Auxílio Emergencial have helped the implementation in an accelerated manner and with a good focus, since we observe a correlation between receiving the assistance and the fact that the beneficiary is set in the lower strata of income.Additionally, it turns out that the professional occupations that experienced the largest loss in the incomes in 2020, were those with a strong tendency towards informality, and therefore, the most impacted by the economic crisis arising from de pandemic.Finally, the analyzes undertaken corroborate the thesis concerning racial and regional inequalities existing in Brazil.It is observed that the black and brown community from the Northeast macro-region constituted the population group that have most required the Auxilio Emergencial in the country.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.172 | 0.002 |
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; both teacher heads agree on what is shown here.
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".