THE IMPACTS OF COVID-19 ON THE PERFORMANCE OF BRAZILIAN FRANCHISING
Bibliographic record
Abstract
The Brazilian franchise system has growth projections and has become an increasingly interesting option for those who want to open their own business (ABF, 2020, ORTEGA ET. AL, 2016). However, with the outbreak of Covid-19, this scenario has changed and the consequences of the pandemic for this market are not known. Therefore, this article aimed to analyze how the impacts of Covid-19 and its consequent social isolation measures affected the performance of the Brazilian franchising market. For this, a diagnostic and comparative analysis of data was carried out, with a descriptive and analytical approach, based on documents research. We analyzed 19 performance reports from the 1st quarter of 2019 to the 1st quarter of 2022, prepared by ABF and available on its website so far. The results point to a large variation in performance during the period, with the 2nd quarter of 2020 being the most affected. The franchise market, however, proved to be resistant, innovative and adaptive, resulting in the total resumption of this system in the 4th quarter of 2021, even surpassing the numbers of 2019. Also noteworthy is the variation between segments, some maintained growth expectations such as Home and construction and Health, beauty and well-being, others were strongly affected such as Hotel and tourism services and Entertainment and leisure, however, there is a gradual pace of recovery in these sectors.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".