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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".