Forgotten forests: expanding potential land use in traditional Hawaiian agroecosystems, and the social-ecological implications
Bibliographic record
Abstract
The development of agricultural systems is a fundamental component of social-ecological transformation and a predominant factor influencing social behavior and structuring. However, oversimplification of traditional agricultural production often occurs and limits the understanding of past populations’ abilities to mitigate potential risks and enhance food security through effective land management strategies. The social-ecological traits that characterize the Hawaiian Islands provides a unique vantage to explore human ecodynamics over the <em>longue durée</em> and assess how these systems can be used to inform current and future land-use strategies, both locally and globally. Using the Hawaiian archipelago as a case study, digitized historical maps depicting a range of crop species and cropping systems were georeferenced to assess previous estimates of land use by early island populations and demonstrate the limitations of narratives constructed from previously modeled extents of land-use activity that rely solely on the preservation of archaeological remnants. The results of our mapped vegetation correspond well with the more intensive forms of agriculture that were included in previous models, but overall indicate that previous models do not fully represent the extent of land use by early island populations, missing vast applications of agroforestry and arboriculture. Based on our findings, we argue that the omission of cultivation systems not associated with physical infrastructure has vastly limited the comprehension of land use by early island populations and driven narratives in social-ecological dynamics that underestimate the extent of agricultural production while inferring sociopolitical outcomes based on the prevailing agricultural dichotomy. To remedy this limitation, we suggest a multimethods approach that integrates diverse data sets for an agricultural model that is more inclusive of all agricultural forms implemented by early Native Hawaiian populations and, therefore, is more representative of the extents of land use by island populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".