El uso de la Inteligencia Artificial en el análisis de impacto normativo.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
El Análisis de Impacto Normativo se implementa en la actualidad en varios países como una herramienta de vanguardia para la mejora de la calidad de la ley. Con el objetivo de explorar el potencial de la Inteligencia Artificial en el ámbito normativo, se analizan las oportunidades de involucrar el virtuoso binomio metodología-tecnología para corregir problemas que afectan a la legislación tales como hiperlegislación, la baja calidad de las normas, la hipostenia y la hipertrofia de los sistemas normativos. Se discuten las limitantes y virtudes de esta metodología de evaluación, para detectar áreas de oportunidad de inclusión de la Inteligencia Artificial, con la finalidad de apoyar el contenido y la forma de la intervención legislativa proyectada, anticipando sus posibles impactos. Esto favorecerá la aprobación de normas con menos probabilidades de fallar.
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.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it