Naturalness Is in the Eye of the Beholder
Bibliographic record
Abstract
World conservation discourse concentrates on forests of high naturalness, which are variously termed intact forest landscapes, primary forests, pristine forests, and wilderness. In this essay, we bring Amazonian Indigenous perspectives to this discussion, both because Amazonian Indigenous Peoples have the right to be in the discussion and because they have a lot to teach us about naturalness. It is essential to understand that Indigenous ontologies do not distinguish culture from nature, since all beings, humans and non-humans, are part of a network of social-ecological interactions. Hence, forests are not natural, but the domus of different beings who inhabit, care for and cultivate them. Each part of the forest mosaic in different stages of social-ecological succession has different owners: when people open swiddens, they must respect other – non-human – forest residents to do so, and when they fallow their swiddens, these other forest residents reassume their original roles as managers and conservers of that part of the mosaic. Each stage of the succession also contains cultivated and domesticated plant populations, so we can think of a different kind of conservation: that of genetic resources. From this perspective, swidden-fallow represents on farm conservation, while less anthropogenic parts of the forest mosaic represent in situ conservation. We believe that reframing forest conservation and learning from Indigenous People can inspire innovative conservation science and policies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".