Privilegiar os saberes ancestrais como forma de enfrentar a crise e promover a preservação ambiental
Bibliographic record
Abstract
O presente trabalho tem como intuito demonstrar que os povos originários possuem saberes ancestrais que podem ajudar a enfrentar a crise ambiental que nos encontramos no momento. Para isso, primeiramente fazemos uma contextualização das mudanças climáticas que vêm ocorrendo mundialmente no último século e as consequências que essas mudanças, também denominadas de eventos climáticos extremos estão ocasionando para a sociedade. A partir disso, discorremos sobre os significados da terra e da natureza aos povos originários e os conhecimentos e práticas que eles compartilham conosco, que são fundamentais à preservação ambiental. Concomitantemente, utilizamo-nos de autores consolidados de diferentes áreas e abordagens, como Donna Haraway, Judith Butler, Rita Segato, Pierre Dardot e Christian Laval, para teorizar e desenvolver a nossa hipótese da importância de ouvir os povos originários e aliar-nos a eles na primordial tarefa de “adiar o fim do mundo”.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".