Relational research to elevate cultural dimensions of marine organisms in Hawaiʻi
Bibliographic record
Abstract
With the increasing focus on elevating local and Indigenous Knowledge and culture in marine management, there has been a growing desire for methods that identify cultural attributes of marine species. We piloted one method to initiate discussion about how cultural connections to marine organisms in Hawaiʻi might be operationalized in conventional marine management systems. We first compared the taxonomic groups mentioned in NOAA management documents with those mentioned in foundational Hawaiian texts. We then used inductive content analysis to examine the cultural meanings associated with those taxa in a foundational primary source for Native Hawaiian fishing practices. Finally, we explored the implications of applying various biocultural frameworks that more explicitly include cultural considerations in conventional management. We discovered a difference in not only the specific marine taxa emphasized in NOAA management documents and those emphasized in Hawaiian texts, but also a difference in the cultural domains that the taxa represent. This mismatch illustrates gaps in conventional marine management with respect to consideration for biocultural aspects of marine taxa. Our study highlights one method that begins to bridge worldviews to ensure cultural dimensions of marine species are examined through Indigenized methodological approaches and place-based values, not just the conventional global frameworks typically used in marine management. Focusing on cultural connections, practices, and heritage not only broadens understanding of the marine environment as a part of a larger social-ecological system, but also enhances the types of science and knowledge considered in natural resource management.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".