O impacto da pandemia de Covid-19 no capital de giro e a previsão de insolvência das companhias brasileiras do subsetor de hotéis e restaurantes
Bibliographic record
Abstract
This study aimed to analyze the short-term financial management of companies in the hotel and \nrestaurant sectors, listed on B3 by the Fleuriet model, and its ability to analyze the possibility of \ninsolvency of entities. The research is classified as descriptive, documentary and quantitative, since \nsecondary data was collected from companies in the hotel and restaurant subsector between the 1st \nquarter of 2018 and the 1st quarter of 2021 from the Economática® database. Thus, using descriptive \nand graphical analysis, the variables of the Fleuriet model were used, which are Working Capital \nRequirement (WCR), Working Capital (WC) and Treasury Balance (TB), as well as the insolvency \nprediction model proposed by Prado et al. (2018). The results evidenced that, by the Fleuriet dynamic \nmodel, all companies, in different quarters, had an undesirable financial situation in 2020, confirming \nthe impact of Covid-19 on their operations. Therefore, society’s understanding of the impact on these \nsegments during the pandemic is eminent, as this information is relevant for users external to \norganizations, strengthening their decision-making power
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".