La administración y financiación de la licencia remunerada por enfermedad
Bibliographic record
Abstract
Resumen. Es necesario que los poderes públicos nacionales que acarician la posibilidad de implantar o reformar la licencia remunerada por enfermedad conozcan los métodos que se usan para costear esta prestación. Basándose en datos mundiales sobre la legislación al respecto, los autores infieren que el tipo de régimen vigente—responsabilidad del empresario, seguro social, asistencia social o una mezcla de ellos—influye mucho en la duración y cuantía de la prestación. Sin embargo, no hallan ninguna relación estrecha de la duración y la generosidad de la licencia por enfermedad con indicadores económicos como el PIB por habitante, la tasa de desempleo o la competitividad nacional. Por último, los autores estudian los instrumentos capaces de garantizar una licencia eficaz y un rendimiento económico óptimo.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".