Characterization and Analysis of Fair Sellers in the Municipality of Janaúba in the North of Minas Gerais, Brazil
Bibliographic record
Abstract
The objective of this work was to characterize and analyze the marketers who market their products at the Janaúba free trade fairs regarding the socioeconomic profile, business management, marketed products and difficulties in marketing. The research was descriptive. The data were collected by means of field survey in the main points where the free fairs in the municipality of Janaúba occur. The obtained data were analyzed through the distribution of frequencies and tables. Farmers are mostly family farmers who do not receive any kind of technical assistance or training to run the crop. They apply their own resources to cover the expenses and investments in production, having as a limiting factor the expansion of the business. Most do not control profitability, and of the percentage that does, only 7% use spreadsheet, do not have financial reserves in case of unforeseen occurrences. The values of the products marketed are almost always uncertain, since 81% of the marketers say that prices are negotiable. The main products marketed are fruits and vegetables, not being processed. Among the difficulties pointed out by the fairgrounds for the execution of the activity are the production costs. Regarding the structure of the Municipal Market, the main negative points were the lack of assistance (32%) and the lack of cleanliness (31%). The main improvement point was the availability of drinking water and the construction of bathrooms.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".