Unchaining supply chains: Transformative leaps toward regenerating social–ecological systems
Bibliographic record
Abstract
Abstract The worsening climate, biodiversity, and inequity crises have existential implications. To help resolve these crises, supply chains must move beyond a minimal harm approach. Instead, supply chains must make positive contributions to and harmoniously integrate with the living systems around them. Despite agreement on this urgent need, supply chain management research still lacks a shared roadmap for establishing economically sustainable supply chains that actively regenerate social–ecological systems. This essay deepens the understanding of regenerative supply chains, inviting supply chain scholars and practitioners to rally around timely questions and codevelop new answers. We first scrutinize the paradigmatic assumptions that continue to anchor contemporary research and practice in supply chain management, showing how these once helpful assumptions now hold the community back from seeking much needed solutions. We then offer real‐world examples and synthesize emerging arguments from multiple disciplines to propose three new principles of regenerative organizing: proportionality , reciprocity , and poly‐rhythmicity . We also delve into the implications of pursuing these regenerative principles for supply chain coordination, governance, and resilience. Finally, we reflect on the fit of empirical research designs and methods for examining the creation of new regenerative supply chains and the conversion of existing supply chains.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".