MétaCan
Menu
Back to cohort
Record W7029743397

La gestión educativa descentralizada en el Perú y el desarrollo de las funciones educativas de los gobiernos regionales: el caso de Ica

2017· article· es· W7029743397 on OpenAlexfundno aff

Bibliographic record

VenueAmericanae (AECID Library) · 2017
Typearticle
Languagees
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsContext (archaeology)Balance (ability)Population
DOInot available

Abstract

fetched live from OpenAlex

El estudio ha tenido como objetivo poner de relieve los desafíos que enfrenta la descentralización educativa en las regiones, analizando de modo particular el funcionamiento y los problemas de gestión de las Gerencias de Desarrollo Social y las Direcciones Regionales de Educación a partir del análisis de un caso. La investigación se llevó a cabo entre el año 2011 y mediados de 2012, un periodo particularmente importante para el proceso descentralizador en el campo educativo, tanto por la coincidencia del inicio de nuevas administraciones políticas en los niveles de los gobiernos central, regional y local, como por los cambios impulsados por la nuevas autoridades del Ministerio de Educación (MINEDU). El estudio, en primer lugar, ha buscado conocer los avances logrados desde los gobiernos regionales en cuanto a la implementación de las funciones educativas a través de las Gerencias de Desarrollo Social (GDS) y las Direcciones Regionales de Educación (DRE). En segundo lugar, se ha querido identificar las principales barreras y dificultades que las instancias intermedias –principalmente las DRE– enfrentan para poder asumir a cabalidad las competencias y responsabilidades transferidas. En relación con esto último, se ha puesto atención al tipo de relaciones de coordinación y articulación entre el nivel central y el nivel regional.

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 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)0.000
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.007
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAmericanae (AECID Library)Same topicLibraries and Information ServicesFrench-language works237,207