Cultivating Ecological Knowledge for Corporate Sustainability: Barilla's Innovative Approach to Sustainable Farming
Bibliographic record
Abstract
Abstract In this paper, we link three theoretical perspectives – organizational knowledge, ecological knowledge and social–ecological systems – to derive new conceptions of multi‐disciplinary, multi‐tier, sustainability‐oriented knowledge. Our study examines how collaboration between pasta‐producer Barilla, the farmers/smallholders supplying the firm and scientists generated sustainability practices in the agri‐food industry by creating transformative ecological, technical and scientific knowledge. In 2010, Barilla initiated a sustainable farming project to significantly reduce the environmental impact of cultivating durum wheat, its most important raw material. Core components included replacing monoculture with crop rotation, collectively creating innovative approaches that support farmers’ decision making and generating widely accessible guidelines for sustainability‐oriented cropping knowledge and practices. These collaborative efforts initiated profound transformations within and beyond the organization's boundaries via increased production yields, reduced environmental impacts and improved sustainability of farming practices, which generated economic, social and ecological benefits for farmers, surrounding communities and the firm. Copyright © 2016 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".