Cultivating an Ecological and Social Balance: Elite Demands and Commoner Knowledge in Ancient Ma‘ohi Agriculture, Society Islands
Bibliographic record
Abstract
ABSTRACT Anthropological views of past human–environmental interactions are influenced by the data sets used and the subjects of study. In this article, we seek a balanced view of ancient human–environmental interactions in the Society Islands. We explore the social and ecological contexts of agricultural production by incorporating archaeological and ethnographic data as well as the motivations and actions of Ma‘ohi elites and commoners. Both elites and commoners contributed to long‐term agricultural productivity. The elite did so through periodic restrictions on harvesting; the farmers contributed ecological knowledge acquired through generations of on‐the‐ground experience. Our fine‐scale examination of the archaeological remains of three agricultural systems in the ‘Opunohu Valley indicates that the roles of elite and commoner played out differently depending on their social‐spatial proximity. By refocusing our analyses on all players in the production system, a more nuanced understanding of the range of ancient environmental and social interactions emerges.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".