Mapping food businesses with regenerative potential in the Amazon and Central American Dry Corridor
Bibliographic record
Abstract
The global food system plays a pivotal role in environmental challenges, being a major contributor to climate change, the primary driver of tropical deforestation, and responsible for one-third of global greenhouse gas emissions. In response to these challenges, a regenerative approach to food businesses has emerged as a promising framework for driving environmental change and addressing the climate crisis. However, there is a gap in information across Latin America regarding the number, location, and activities of food businesses adopting a regenerative approach, hindering a better understanding of this trend and limiting its potential support in the region. This article presents the results of a mapping effort using specific criteria and analytical frameworks to build a better understanding of how regenerative food business models are evolving in Latin America. The mapping was conducted across six countries in the Central American Dry Corridor and five in the Amazon Biome. The process involved using the Google search engine with 77 keyword combinations, complemented by information from 50 key informant interviews. A total of 181 businesses with a potentially regenerative focus were identified. Of these, 64 were explicitly using the term “regenerative,” with its usage being more prevalent in the Central American Dry Corridor than in the Amazon. Notably, businesses using the term were non-associative enterprises. In contrast, associative enterprises such as cooperatives and associations, although not employing the term “regenerative,” played a critical role in socio-cultural and environmental conservation of territories, particularly when led by indigenous or other local traditional populations. Furthermore, the participation of women in leading these businesses was higher than in other traditional businesses, though it still reflected global gender imbalances in leadership positions compared to men. This study provides one of the first comprehensive mappings of regenerative food businesses in the Amazon and CADC, offering valuable data from Latin America. The findings reveal the distribution, characteristics, and diverse ways businesses engage with regenerative practices, underscoring the need for further research beyond the explicit “regenerative” term to fully capture the scope of initiatives driving socio-environmental transformation in the region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".