Construyendo una burocracia más eficaz en Chile: lecciones del caso de Singapur
Bibliographic record
Abstract
Chile tiene un notable registro histórico de bajos niveles de corrupción. Sin embargo, el Estado chileno enfrenta problemas de modernización, como se refleja en los esfuerzos actuales de reforma. Este artículo ofrece un esbozo de ciertas características del servicio civil de Singapur, reconocido como uno de los más efectivos del mundo, para que sea considerado en el contexto de este proceso. El artículo examina brevemente los sistemas de Singapur en reclutamiento, evaluación y promoción y sistemas de formación. En comparación con Chile, encontramos importantes contrastes, incluyendo la existencia de un empleo permanente, altos niveles de competencia, que reflejan en una meritocracia medible objetivamente, una capacitación vigorosa, y un fuerte sentido de misión pública, respaldado por la capacidad de desarrollar una visión a largo plazo.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".