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Record W3041826960 · doi:10.22370/rgp.2012.1.2.2333

Sanciones administrativas como mecanismo anticorrupción: el caso de México a nivel federal, 2005-2008

2020· article· es· W3041826960 on OpenAlex
David Arellano Gault, Walter Lepore, Israel Aguilar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRevista de Gestión Pública · 2020
Typearticle
Languagees
FieldSocial Sciences
TopicSocial Issues and Policies in Latin America
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Las sanciones a los servidores públicos con el fin de detectar tanto fallas o errores en su accionar como actos de corrupción son, sin duda, un instrumento de vigilancia básico en cualquier democracia. Para ello, sin embargo, se deben considerar los retos organizacionales y de estrategia que la implementación de estos instrumentos implica. Es decir, la simple existencia de la norma no es suficiente pues el detalle estará en para qué y cómo se utilizan estos instrumentos para lograr sus objetivos (reducir la incapacidad administrativa y la corrupción por ejemplo). Este artículo presenta un primer esfuerzo por analizar las sanciones aplicadas a los servidores públicos federales en México de 2005 a 2008, con el fin de estudiar e identificar si existen criterios básicos a través de los cuales el gobierno federal mexicano está utilizando este instrumental de control y supervisión. La conclusión básica es que no existe, aparentemente, una estrategia explicita que defina criterios básicos y uniformes para la aplicación de estas sanciones, por tanto dejando muy endeble la lucha contra la corrupción o la mejora administrativa a través de estos instrumentos.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.035
GPT teacher head0.358
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it