A practical tool to assess regenerative approaches in food businesses
Bibliographic record
Abstract
Food systems account for approximately 34% of global greenhouse gas emissions. This figure in itself stresses the urgent need for effective solutions that mitigate impacts while ensuring food security. Regenerative Agriculture and Regenerative Food Businesses are emerging as promising approaches to address this challenge. However, it is essential to develop accessible methods to gather and standardize information on regenerative framework to gain stakeholder support and encourage business adoption. This article presents the Initial Perception of the Regenerative Approach (IPRA) tool, designed to provide a rapid and preliminary assessment of the alignment of food businesses in Latin America and the Caribbean (LAC) with the regenerative approach. IPRA evaluates whether a company’s actions, intentions, and narrative are aligned of regenerative principles and practices. Its goal is to generate sufficient data, at low cost, to enable different institutions to analyze and identify business models that align with their interests, prior to investing in more in-depth field studies. The tool comprises four main instruments that support the systematic collection of business information and the evaluation of regenerative attributes across environmental, social, and economic dimensions. A scoring system (0–4) is used, integrated with qualitative data from interviews. The IPRA was employed in the analysis of 55 food businesses drawn from a previous mapping of 181 businesses across the Amazon and Central American Dry Corridor. The results showed that the tool is capable of providing a general overview of the regenerative approach adopted by businesses, as well as enabling comparisons among them. It also serves as a useful resource for stakeholders seeking a deeper understanding of businesses they might be interests in. These findings revealed varying levels of alignment among the businesses, with an overall correspondence with regenerative practices highlighted in existing literature, particularly in agronomic and environmental aspects. The tool proved adaptable, effective, and cost-efficient for gathering data across the food system, including agricultural production, forest food gathering, or commercial processing. This rapid overview offered by the IPRA could play a key role in supporting the urgent development of public policy frameworks and other actions aimed at strengthening and advancing the regenerative approach throughout LAC.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".