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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".