Engaging with Nature in Times of Rapid Environmental Change: Vulnerability, Sentience and Autonomy
Bibliographic record
Abstract
Los cambios ambientales cada vez más rápidos desde mediados del siglo xx plantean un \ndesafío importante para las poblaciones humanas vulnerables. Los nativos de América de la \ncosta noroeste, como muchas otras poblaciones indígenas en todo el mundo, han concebido \nlos paisajes como seres sensibles y capaces de responder a la acción humana. Aquí exploramos \nla consecuente “responsabilidad social” por el paisaje en el contexto de vulnerabilidad al \ncambio ambiental rápido. Se debate la base del respeto que subyace este sentido de responsabilidad, \ny su importancia para abordar la vulnerabilidad humana frente a la agencia de \nla naturaleza, a través de prácticas más adecuadas de mitigación y adaptación. Se concluye \nque enfrentamos un imperativo de reconcebir la agencia de los fenómenos naturales.
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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 | 0.000 | 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) | 0.001 | 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".