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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".