Incorporating impoverished communities in sustainable supply chains
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore recent calls to include social and environmental considerations in supply chains by analyzing the sourcing of raw materials from impoverished communities to reduce environmental impacts and social exclusion in biofuels production. Design/methodology/approach A case study methodology based on interviews and focus groups with supply chain members and other stakeholders is conducted in Brazil, a major biofuels producer and user. Two supply chain cases, fuel ethanol and biodiesel, illustrate the challenges of recent government policies and industry attempts to improve sustainability within the supply chain. Findings Although government and industry recognize the importance of providing opportunities for impoverished communities in biofuels supply chains, there remain considerable pressures to economize at the expense of sustainable supply chain policies. Sourcing from impoverished farmers who lack basic business knowledge, and distrust industry and government policy, compound these challenges. Research limitations/implications While sustainability research now emphasizes the importance of considering interactions among economic, environmental, and social parameters, little is known about integrating poorly educated, impoverished farmers within supply chains. Basic business education is needed, and further research should explore entrepreneurial dynamics within impoverished communities. Practical implications Supply chain managers should acquire skills for engaging with impoverished farmers lacking formal education. Cooperatives can bridge knowledge asymmetries between buyers and suppliers, but will require support from industry if sustainable supply chain policies are to succeed. Originality/value Most sustainable supply chain scholars acknowledge the importance providing opportunities for impoverished communities, but few have explored how potential entrepreneurs from impoverished communities can participate as productive supply chain members.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".