Qué indicadores TIC pueden mejorar la productividad en España
Bibliographic record
Abstract
Espana ha dedicado en los ultimos anos importantes esfuerzos a traves de planes de desarrollo de la Sociedad de la Informacion a incrementar el nivel de adopcion de las TIC en toda la sociedad. Pero la simple adopcion de las TIC no basta para la mejora del nivel de productividad espanol. Este elevado nivel de adopcion (por ejemplo, el 94% de las empresas espanolas tienen banda ancha) deberia estar acompanado por una utilizacion optima de las nuevas tecnologias para solucionar la paradoja detectada en el caso espanol en cuanto a sus indicadores TIC y su nivel de productividad comparado con otros paises europeos como Noruega. Por ello, se puede apuntar que la «paradoja espanola de la productividad» reside en que el avance de los indicadores TIC se ha centrado sobre todo en la adopcion de las tecnologias, cuando el factor mas relevante para que este avance en TIC repercuta en la productividad y la competitividad es la utilizacion real que se hace de las tecnologias TIC y el grado de familiaridad y uso de las mismas de los ciudadanos, tanto trabajadores como consumidores.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".