Cooperating With “Open Cards”—The Role of Small Intermediary Businesses in Realizing Sustainable International Coffee Supply
Bibliographic record
Abstract
Despite improvements, international food supply in general and coffee supply in particular continue to cause significant greenhouse gas emissions, economic inequities, and negative impacts on human well-being. There is agreement that dominant economic paradigms need to change to comply with the sustainability principles of environmental integrity, economic resilience, and social equity. However, so far, little empirical evidence has been generated to what extent and under which conditions sustainable international coffee supply could be realized through small intermediary businesses such as roasteries, breweries, and/or retailers. This case study reports on a collaborative project between a small coffee brewery and its customers in the U.S. and a small coffee roastery and its suppliers in Mexico that demonstrates how sustainable coffee supply could look like and explores under which conditions it can be realized. A research team facilitated the cooperation using a transdisciplinary research approach, including field visits and stakeholder workshops. The project (i) assessed the sustainability challenges of the current supply and value chains; (ii) developed a vision of a joint sustainable coffee supply chain; (iii) build a strategy to achieve this vision, and (iv) piloted the implementation of the strategy. We discuss the project results against the conditions for sustainable international coffee supply offered in the literature (why they were fulfilled, or not). Overall, the study suggests that small intermediary coffee businesses might have the potential to infuse sustainability across their supply chain if cooperating with “open cards.” The findings confirm some and add some conditions, including economic resilience through cooperation, problem recognition, transparency, trust, and solidarity across the supply chain. The study concludes with reflections on study limitations and future research needs.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".