Bibliographic record
Abstract
La distinción entre normas y proposiciones normativas es un lugar común en la teoría analítica del derecho, junto con la idea de que las segundas describen a las primeras. Pero pocos autores procuran especificar cuidadosamente en qué consiste la actividad de describir normas. Este trabajo intenta ser una contribución a la comprensión de esa cuestión, buscando distinguir diversos modos en que puede entenderse la descripción de normas jurídicas y preguntándose si algunos de ellos resultan más correctos o útiles que otros. En el texto se distinguen cuatro modos de descripción de normas y se sostiene que su corrección o utilidad está relacionada con el contexto comunicativo en que una descripción es ofrecida: es la interacción pragmática entre los hablantes lo que determina el nivel de información requerido y su utilidad para los objetivos que los participantes se proponen.
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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".