Hacia un modelo ideal de identidad fiscalizadora superior para el siglo XXI
Bibliographic record
Abstract
Esta tesis doctoral se ha estructurado en dos partes bien diferenciadas. Una primera parte, bastante descriptiva en la que he realizado un estudio comparado de las EFS de los paises de Estados unidos, Gran Bretana, Canada, Francia, Alemania, Suecia y Espana. Estas EFS no se han seleccionado al azar o sin ningun tipo de criterio. Se han seleccionado estas FS por pertenecer a modelos diferentes de EFS y por ser las mas modernas en cuanto a modo de entender la mision que tienen encomendada y a los metodos y tecnicas utilizados. La segunda parte de la tesis doctoral, que tiene su origen en la primera, consiste en la exposicion de una serie de conclusiones que sintetizan el mencionado estudio comparado. Esta segunda parte se cierra con la formulacion de una veintena de propuestas destinadas a hacer del Tribunal de Cuentas de Espana una EFS ideal para nuestro pais. Las mencionadas propuestas estan mayormente basadas en todo el trabajo desarrollado en la primera parte.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".