La visión comparada: nuevos sistemas de selección de la alta función pública en las democracias avanzadas
Bibliographic record
Abstract
espanolEl articulo realiza un examen comparado de sistemas de seleccion en paises de nuestro entorno que constituyen un referente de buenas practicas en la gestion por competencias de los recursos humanos. Se parte para ello de un breve diagnostico de las debilidades de nuestros procesos selectivos y se describen experiencias comparadas que pueden orientar futuras reformas. En la fase de definicion de las necesidades y perfiles se describe la experiencia francesa de gestion planificada de efectivos y competencias, que ha dado lugar al Repertorio Interministerial de Profesiones del Estado y al Diccionario Interadministrativo de Competencias de las Profesiones del Estado. En la fase de reclutamiento se examinan las actuaciones dirigidas a reforzar la marca como empleador de la administracion federal canadiense, y los diferentes programas desarrollados en el Reino Unido para asegurar la diversidad social del empleo publico. En la fase de seleccion se analizan los modelos de Francia, Reino Unido, Belgica, Irlanda y las instituciones europeas, asi como el modo en el que estas experiencias podrian trasladarse a nuestros sistemas selectivos EnglishThe article carries out a comparative examination of selection systems in neighboring countries that consti-tute a reference of good practices in human resources competency-based management. We start with a brief diagnosis of the weaknesses of our selection processes and describe comparative experiences that can guide future reforms. In the definition phase of needs and profiles, the French experience of planned management of staff and competencies is described, which has led to the Interministerial Inventory of State-sector Jobs and the Interadministrative Dictionary of Competencies of the State-sector Jobs. The recruitment phase examines the actions aimed at strengthening the employer brand of the Canadian federal administration, and the different programs developed in the United Kingdom to ensure the social diversity of public employment. In the selection phase the models of France, the United Kingdom, Belgium, Ireland and the European institutions are analyzed, as well as the way in which these experiences could be transferred to our selection systems euskaraArtikuluak hautapen-sistemen azterketa konparatua egiten du giza baliabideen gaitasunen araberako kudeaketan jarduera onak egiten dituzten inguruko herrialdeetan. Horretarako, gure hautapen-prozesuen ahultasunen diagnostiko laburra egiten da aurrena, eta etorkizuneko aldaketak bideratu ditzaketen esperientzia konparatuak azaltzen dira gero. Premiak eta profilak zehazteko fasean, Frantziak langileak eta gaitasunak modu planifikatuan kudeatzeko duen eskarmentua azaltzen da, Estatuko Lanbideen Ministerioarteko Errepertorioa eta Estatuko Lanbideen Gaitasunen Administrazioarteko Hiztegia ekarri baititu. Errekrutatzefasean, Kanadako administrazio federaleko enplegatzaile gisa marka indartzeari zuzendutako jarduerak aztertzen dira, bai eta Erresuma Batuko enplegu publikoan gizarte-aniztasuna egotea ziurtatzeko garatu diren hainbat programa aztertu ere. Hautapen-fasean, Frantziako, Erresuma Batuko, Belgikako, Irlandako eta Europako erakundeen ereduak aztertzen dira, eta esperientzia horiek gure hautapen-sistemetara ekartzeko moduak esploratzen
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.006 |
| 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".