O IMPACTO ECONÔMICO NAS MICROEMPRESAS BRASILEIRAS DURANTE A CRISE DA COVID-19 E AS MEDIDAS ADOTADAS POR PARTE DO GOVERNO FEDERAL NO MERCADO DE TRABALHO
Bibliographic record
Abstract
This paper aims to address the economic impact during the health and economic crisis of COVID-19, in the Brazilian micro-enterprises raised by SEBRAE, as well as to understand what measures were adopted by the Federal Government and the Ministry of Economy to reduce this shock, in the context of the labor market. Methodologically, we use the inductive method, using the procedure of presenting some data capable of sampling the dimension of the current situation, combined with a quantitative research applied by SEBRAE / FGV, a study in which the consequences of COVID-19 for the operation of micro-enterprises. As part of the results of our investigation, we see that the aggravation of the current situation would be more easily overcome, if the majority of the population were engaged in formal jobs and had a financial stability different from the reality that the country is experiencing.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".