Empowering Indigenous agency through community-driven collaborative management to achieve effective conservation: Hawai‘i as an example
Bibliographic record
Abstract
Indigenous peoples and local communities (IPLCs) around the world are increasingly asserting ‘Indigenous agency’ to engage with government institutions and other partners to collaboratively steward ancestral Places. Case studies in Hawai‘i suggest that ‘community-driven collaborative management’ is a viable and robust pathway for IPLCs to lead in the design of a shared vision, achieve conservation targets, and engage government institutions and other organisations in caring for and governing biocultural resources and associated habitats. This paper articulates key forms of Indigenous agency embodied within Native Hawaiian culture, such as kua‘āina, hoa‘āina, and the interrelated values of aloha ‘āina, mālama ‘āina, and kia‘i ‘āina. We also examine how Hawai‘i might streamline the pathways to equitable and productive collaborative partnerships through: (1) a better understanding of laws protecting Indigenous rights and practices; (2) recognition of varied forms of Indigenous agency; and (3) more deliberate engagement in the meaningful sharing of power. We contend that these partnerships can directly achieve conservation and sustainability goals while transforming scientific fields such as conservation biology by redefining research practices and underlying norms and beliefs in Places stewarded by IPLCs. Further, collaborative management can de-escalate conflicts over access to, and stewardship of, resources by providing IPLCs avenues to address broader historical legacies of environmental and social injustice while restoring elements of self-governance. To these ends, we propose that government agencies proactively engage with IPLCs to expand the building of comprehensive collaborative management arrangements. Hawai‘i provides an example for how this can be achieved.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".