Bibliographic record
Abstract
El siguiente texto, basado en una conferencia realizada el 31 de octubre de 2012 en la Facultad de Humanidades y Cs. Sociales de la Universidad Nacional de Misiones, ofrece un planteamiento programático dentro de los debates sobre la ciudadanía ambiental. Comienza con una contextualización de los debates dominantes en torno a la ciudadanía ambiental, y propone otra perspectiva distinta para repensar la relación entre naturaleza y subjetividad política. La segunda parte de la conferencia, plantea una consideración de espacio y escala como dimensiones fundamentales para poder entender esta relación. Recogiendo acontecimientos del trabajo de otros investigadores en varias partes de la región latinoamericana, el análisis explora tres ejemplos de cómo el estudio de espacio y escala puede arrojar luz en las políticas ambientales y en la emergencia de nuevas formas de ciudadanía en esta arena.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".