Pili‘Oha/Kinship: (Re)Imagining Perceptions of Nature and More-Than-Human Relationality
Bibliographic record
Abstract
This essay draws from a larger ethnographic study looking at the complex contextuality of biodiversity conservation in Hawaii. This article uses vignettes to communicate its focus. These vignettes are autoethnographic by nature, but are pushed further through the use of diffractive methodology (Barad) to include social-media visuals, multispecies encounters, and Native Hawaiian (kanaka maoli) perspectives. Through my ontological and methodological approach, I seek to challenge normative discourses on human exceptionalism, nature-culture dichotomies, and the manner in which industrialized societies place hierarchies on species and matter, as well as how these conversations might impact conservation. In this article, and through these vignettes, I explore what it means to be native and how my own positionality situates studying the social and cultural milieu of conservation issues in Hawaii.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".