Insights Gained from Including People in Our Models of Nature and Modes of Science
Bibliographic record
Abstract
Across the natural sciences, humans are typically conceptualized as external disruptors of nature rather than adaptable components of it. Historical evidence, however, challenges this dominant schema. Here, we describe the broad repertoire of ecological functions performed by people in place-based societies across the Pacific Ocean over millennia, illustrating their roles as ecosystem engineers, dispersers, bioturbators, nutrient cyclers, predators, and herbivores. By considering the reciprocal relationships between people and the ecosystems within which they are embedded, evidence of humanity's ability to experiment, learn, adapt, innovate, and sustain diverse and resilient social-ecological relationships emerges. Therefore, recognizing people as inseparable components of marine ecosystems and their millennia of engagement with coastal ocean spaces is critical to both understanding marine ecosystems and devising resilient and equitable ocean policies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.012 |
| 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; 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".