El uso de la Inteligencia Artificial en el análisis de impacto normativo.
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.
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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 | 0.034 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".