O MODELO DE FLEURIET E OS EFEITOS DA PANDEMIA DO COVID-19
Bibliographic record
Abstract
The present study aims to investigate the impact of the COVID-19 pandemic on the financial structure of publicly traded Brazilian companies listed on the IBrx-100, through the analysis of the Management Balance Sheets (BPG) and applying the dynamic Fleuriet model of analysis of working capital. Therefore, the research typology is descriptive and documentary, as it used the Consolidated Balance Sheets as a data source. The data were obtained through the Economática® database and the Reference form available on the website of Brasil, Bolsa, Balcão (B3), thus, the final sample consisted of 76 companies, over 10 quarters, corresponding to the 1st quarter of 2019 to 2nd quarter of 2021. As an analysis technique, a qualitative and quantitative approach was adopted, using descriptive analysis applied to the Fleriet model. The survey results show that over the 10 quarters analyzed, the variables cash balance (ST), working capital need (NCG) and working capital (CDG) were respectively negative by 43.96%, 12.20% and 12.07% of the sample. In addition, there was a predominance of type 2 BPG with 45.01% of the sample, followed by type 3 with 32.41%. As well as the reduction in the number of BPG with the type 3 profile, and the increase in type 2 over the periods. Thus, based on the results exposed in this study, it was identified that a trend of change in the typology of companies, after the beginning of the COVID-19 Pandemic, increasing the profiles considered solid.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".