Individual Micro-Entrepreneur of Snacks and Portions in Brazil: Post-Pandemic Implications and Threats of the New Coronavirus (SARS-CoV-2: COVID-19)
Bibliographic record
Abstract
The individual micro-entrepreneurs constitute a relevant portion of small businesses in Brazil, and despite its relevance, little information was found about the behavior of this sector in the recovery of the economy. Thus, in order to support a better understanding of the current scenario a descriptive exploratory research was carried out with 25 micro-entrepreneurs between June and September 2022. The study revealed that the average age of the interviewees was 41 years old, with the monthly net income obtained between USD 800 and USD R$ 1799. The total of all respondents reported that their ways of life and their businesses were affected during the pandemic, and in the recovery of the economy, they reported in their perceptions that the main implications resulting from that period were the high workload dedicated to work activities, the financial instability of sales , personal stress with the post-pandemic crisis and the lack of skilled labor. With regard to the future, and on the prospects for commercial growth, the majority of the respondents (n=88%) attested that despite being affected by the pandemic, they managed to implement actions to recover revenue, with the personalized customer service, quality of product offered, the variety of food offered and the speed in customer service being the main factors that have facilitated the recovery process after the pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".