Island and Indigenous systems of circularity: how Hawaiʻi can inform the development of universal circular economy policy goals
Bibliographic record
Abstract
Given the dire consequences of the present global climate crisis, the need for alternative ecologically based economic models could not be more urgent. The economic and environmental concerns of the circular economy are well-developed in the literature. However, there remains a gap in research concerning the circular economy’s impact on culture and social equity. The underdeveloped social and cultural pillars of the circular economy, along with universal policy goals calling for a context- and need-based framework, makes it necessary to bridge natural and social science objectives in the circular economy. Islands can serve as model systems for studying the circular economy. We examine how Hawaiʻi, through the philosophy of aloha ʻāina, the Hawaiian ancestral circular economy, and contemporary community approaches toward advancing Indigenous economic justice can be one model system for understanding principles of circularity and policy advocacy. We introduce the concept of the ancestral circular economy and how aspects of this Indigenous institution can inform the development of universal circular economy policy goals. Furthermore, we present aloha ʻāina as a framework for reciprocal care between human–environment relations while addressing the social and cultural pillars that aid in the development of these dimensions of the circular economy.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".