Movimientos indígenas y respuestas estatales frente a la pandemia en México, Ecuador y Brasil
Bibliographic record
Abstract
La pandemia de la covid-19 puso de manifiesto el rol del Estado y de los Gobiernos en la gestión de esta situación de emergencia, así como la capacidad de los movimientos sociales para proponer formas de acción colectiva, de autonomía y de solidaridad. Con base en la observación de medios digitales pertenecientes a movimientos indígenas en México, Ecuador y Brasil –entre marzo de 2020 y julio de 2021– se analiza cómo estos movimientos han reorganizado sus formas de resistencia y han llevado a cabo acciones basadas en sus demandas, al visibilizar la ausencia y negligencia del Estado y establecer alianzas o asumir el reto de manera autónoma. Los casos abordados en el presente artículo permiten ampliar la comprensión sobre la potencia e importancia del accionar de los movimientos sociales, y, a la vez, proponer un entendimiento de la pandemia que atraviesa múltiples dimensiones sociales y ambientales, más allá de la cuestión sanitaria.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".