El lenguaje y el discurso literario al servicio de la condena histórica
Bibliographic record
Abstract
El lenguaje es una herramienta fundamental en la construcción del discurso histórico. En el caso de la conquista americana, este discurso comprende las voces de unos seres humanos que comparten no sólo su participación en el proceso de descubrimiento y conquista, sino internacionalidades semejantes semejantes a la hora de constrsus textos. Su reivindicación como sujetos del sidcurso hitoriográfico se alinea con una visión centralista del mundo: sus hazañas, no la sangre los justifican. Sin embargo, para aquellos como Lope de Aguirre, quienes plantean una forma de oposición a esa visión unificadora, el lenguaje se utiliza con toda su fuerza para acallar y resignificar su participación en la historia. Este trabajo busca mostrar cómo, en efecto, el discurso literario individualiza a Aguirre como un ser excepcional en el sentido de los monstruoso y violento en el ejercicio ilegítimo su poder. Su representación en el terreno de lo periférico y de la irracionalidad lo diferencia de los otros objetos del discurso, aquellos de origen indígena a quienes se les niega su alteridad desde el principio.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".