Social Learning and Paulo Freire Concepts for Understanding Food Security Cases in Brazil
Bibliographic record
Abstract
Food insecurity is a wicked, complex, and critical problem. Although evidence supporting a wide range of assertions regarding the outcomes of social learning is still being investigated, its potential to improve food security challenges is growing. Nonetheless, more work is needed to understand when and how social learning-oriented approaches are effective in food security situations. We address this gap by investigating how elements of social learning and Freire’s key concepts are exemplified in existing real-world experiences of food security in rural communities. The case studies in Brazil, Community Seed Banks in Paraíba State, in the northeast and Biodiversity Kit in Guaraciaba, Santa Catarina State, in the south, are examples of small farmers facing and overcoming their limit-situation of food insecurity through celebrating, planting, and saving traditional seeds (landraces). A mixed-methods approach was applied based on semi-structured interviews and a literature review. The key findings show that local initiatives based on the interconnections of social learning and Freire´s concepts have improved food security in two cases. The practice of landrace rescue as a food security strategy is strengthened through a culture of closeness and solidarity, through values that are celebrated in the festivities, community meetings, and other exchanges of experiences. Applications of our conceptual framework in operational interventions show clear potential for generating the necessary changes for a more sustainable world, specifically in food security and sovereignty projects, as described in the cases studies.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".