Healthy eating through an alternative food network at Agricultural Fair
Bibliographic record
Abstract
The Alternative Food Network (RAA) works collaboratively from the production to the consumption of healthy food from the countryside to the city, unlike the Industrial Agrifood System. Through the RRA, socio-productive inclusion occurs in the promotion of Territorial Agro-Food Systems (SAT) with emphasis on ecological practices, social technologies and Short Circuits (CC). This cooperation network leads to the promotion of healthy eating with Sovereignty and Food and Nutrition Security (SSAN). In this sense, the objective of the research was to analyze the execution of the FIAV Virtual Agroecological Interinstitutional Fair during the year 2021 as a strategy to promote healthy eating, through the methodology of existential/integral action research that started due to the COVID-19 pandemic. The methodology chosen was the existential/integral action research, carried out in 2021 and 2022 with the family farmers involved. FIAV demonstrated the potential of the agroecosystems in the municipalities of Vianópolis, Silvânia, Campestre and Palmeiras (Goiás-Brasil) in offering regional, seasonal foods with nutritional value and produced with ecological practices by family farmers. There were ten editions held at the Federal Institute of Goiás (IFG), the Federal Institute of Goiás (IF Goiano) and the Federal University of Goiás (UFG). FIAV promotes healthy eating with SSAN, however, among the challenges encountered, there is that of family farmers resisting the socio-environmental impacts of the Industrial Agrifood System.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".