Bibliographic record
Abstract
Este artículo analiza ejemplos seleccionados de usos de tácticas argumentativas que explotan lenguaje emotivo, muchas de las cuales han sido criticadas como engañosas e incluso falaces por fuentes clásicas y recientes, incluyendo manuales de lógica informal actuales. El análisis se basa en seis esquemas de argumentación y en una explicación del marco dialéctico en el que se usan esos esquemas. Las tres conclusiones son (1) que tales usos de lenguaje emotivo son a menudo razonables y necesarios en la argumentación que se basa en valores, (2) pero que son derrotables y, por tanto, han de considerarse abiertos a preguntas críticas (3) y que cuando se usan falazmente es porque interfieren con el cuestionamiento crítico u oculta la necesidad de éste. El análisis ofrece criterios para distinguir entre argumentos basados en el uso de palabras emotivas que son herramientas razonables de persuasión y tácticas falaces usadas para ocultar y distorsionar información.Palabras clave: Lenguaje cargado, redefiniciones, eufemismos, persuasión, argumentación.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".