¿Sirve de algo planificar? los déficits de la gobernanza anticipatoria y la proactividad administrativa
Bibliographic record
Abstract
espanolLacrisis ocasionada por el virus COVID-19 ha puesto en evidencia la falta de capacidad de anticipacion de nuestra gobernanza multinivel, a pesar de las advertencias y los planes aprobados hace mas de una decada para saber como actuar en situaciones de este tipo. El protagonismo de los expertos no ha estado acompanado por una metodologia apropiada de toma de decisiones (mas participativa y no tan centralizada), para la que el estado de la ciencia de definicion estrategica de escenarios podria haber sido util. Los ejemplos comparados de paises mas desarrollados (Canada, Finlandia ...) tambien muestran iniciativas para intentar evitar los errores del pasado, no muy utiles sin embargo para prevenir esta crisis sanitaria. PALABRAS EnglishThe COVID crisis has revealed the inability to anticipate of our multilevel governance, despite warnings and plans approved over a decade ago to know how to act in these situations. The leading role of experts has not been accompanied by and appropriate decision-making methodology (more participatory and no so centralized), for which the sta te of science strategic scenario definition could have been useful. Today, the comparative example of the most developed countries (Canada, Finland) show us initiatives to avoid the same mistakes of the past, but not to prevent the COVID-19 crisis.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".