Bibliographic record
Abstract
This article aims to analyze the economic and financial impacts of the pandemic on the number of opening and closing businesses in Brazil. For the development of this study, the data was collected from the Ministry of Economy on the history of companies that opened and closed from 2010 to 2020; monthly sales for 2019 and 2020 with NF-e published by the Internal Revenue Service; IBGE fortnightly survey on the effects of the pandemic on sales in June to August 2020. In addition, an analysis of the relationship between the monthly flow of companies closed in 2020 and the monthly inflation index was carried out. The results point to an increase in the opening of companies “out of necessity” in periods of economic crisis; decrease in NF-e sales in the third quarter, followed by growth from the second half of the year; the greater negative impact of the pandemic on companies with few employees and linked to Construction and Commerce. The analysis of the relationship between the monthly flow of the companies closed in 2020 and the monthly inflation rate was low (r = 0,4231).
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".