Constellating cultivation: texturing agroecological legacies with a mixed-methods approach in Hawaiʻi
Bibliographic record
Abstract
Indigenous agroecological practices have been identified as sustainable place-based practices supporting community resilience and revitalizing biocultural landscapes in Hawaiʻi. In areas where traditional knowledge and practice have been fractured because of colonial practices, new tools, such as spatial modeling, can aid in understanding the distribution of pre-colonial land use. Currently, spatial models of traditional agriculture do not capture the type of systems that supported populations in the Hilo and Puna districts on Hawai‘i Island before European arrival in 1778. We utilized a mixed methods approach to address these limitations by considering Kalapana in the Puna District on Hawaiʻi Island as a case study. First, various forms of agriculture within Kalapana were identified and characterized based on written documentation in both English and ʻŌlelo Hawaiʻi. We then reconstructed spatial patterns of the distinct forms of cultivation by incorporating historic maps, archaeological reports, and botanical data from contemporary ground and remote survey data. Finally, we approximated environmental thresholds for agroecological adaptations in the under-studied Kalapana geography. Our findings suggest that the role of cultivated lava and forest systems would have provided substantial contributions necessary for supporting the populations that resided within the Puna district, spanning a time period of pre-European contact onward well into the 20th century. This study highlights the resilience and ingenuity of rural Hawaiian communities that supported themselves within sustainable, place-based practices and actively stewarded their biocultural landscape over time.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".