Releasing supply chain regeneration through indigenous polyrhythmic governance
Bibliographic record
Abstract
This research investigates how indigenous knowledge, and practices offer crucial insights into reshaping supply chains and advancing toward a sustainable, post-growth era. In framing the existing literature on supply chains and indigenous knowledge around the Brazil nut production system, we aim to explore the following questions: How can supply chains be reimagined to support sustainable practices and equitable governance in a post-growth era? How do indigenous knowledge systems shape regenerative supply chain models for a post-growth future? By adopting an ethnographic approach methodology, the study draws on immersive fieldwork in the Amazon rainforest, including interviews, participatory workshops, and documentary analysis, to examine three layers of "encapsulation": the forest relationship, cooperative structure, and market contracts. These layers reveal the interplay between governance systems, indigenous ways of organising, and external market dynamics applied to the Brazil nut supply chain. The Paiter Suruí cooperative exemplifies a governance model that balances ecological preservation with market participation. The study contributes to supply chain literature by introducing the concept of "polyrhythmic governance," emphasising the integration of diverse knowledge systems. It underscores the need for equitable frameworks that respect indigenous agency, offering pathways for sustainable and inclusive economic practices in a post-growth era.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".