Territorialités dans le Haut Rio Negro : Quelques notes sur les traductions (et les dissonances) dans la démarcation et la gestion des Terres autochtones
Bibliographic record
Abstract
L’article explore la complexité multilingue et multiethnique du Haut Rio Negro, une région de l’Amazonie brésilienne, en mettant l’accent sur la manière dont les différentes communautés autochtones interagissent avec l’État et les politiques publiques concernant la démarcation de leurs terres. L’autrice reprend certaines idées de Lévi-Strauss sur la mythologie et l’histoire pour réfléchir sur le processus de démarcation des terres et certaines de ses implications après environ 25 ans. À travers des exemples de traductions interculturelles faites par des leaders autochtones, l’article montre comment les Autochtones réinterprètent les concepts d’État pour les adapter à leurs réalités vécues, révélant un processus dynamique de négociation culturelle. Enfin, l’autrice réfléchit sur certaines relations entre mythologie et histoire, en apportant des nouvelles significations aux concepts de terre, de territorialité et de gouvernance autochtone dans un contexte contemporain, où les récits mythiques et les discours politiques continuent de dialoguer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".