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Record W2761491282 · doi:10.15366/riejs2012.1.1.009

Educación y justicia social

2015· article· es· W2761491282 on OpenAlexaboutno aff
Pablo Latapí

Bibliographic record

VenueRevista Internacional de Educación para la Justicia Social · 2015
Typearticle
Languagees
FieldSocial Sciences
TopicSocial Sciences and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

(19 de abril de 1927 - 3 de agosto de 2009) Fue doctor en filosofía con especialización en ciencias de la educación por la Universidad de Hamburgo (Alemania) y pionero de la investigación educativa en México. Desarrolló una intensa labor de difusión educativa en medios escritos (Periódico Excélsior, Revista Proceso) radio y televisión.Fundador del Centro del Centro de Estudios Educativos (CEE) en 1963. Realizó por más de 40 años investigación educativa, contando con mas de 30 libros y cerca de 100 artículos publicados. Desde 1985 fue miembro del Sistema Nacional de Investigadores (Nivel III); investigador emérito de la Universidad Nacional Autónoma de México (1996) e investigador nacional de excelencia (2003).Ha sido formador de investigadores y promotor de instituciones e instituciones e iniciativas. Impulsor de los Congresos Nacionales de Investigación Educativa y fundador de la Revista Latinoamericana de Estudios Educativos. Estuvo vinculado con las principales organizaciones nacionales e internacionales del campo educativo (UNESCO, UNICEF, OEI, OEA), siendo embajador entre los años 2006-2007 del gobierno mexicano en UNESCO.Fue asesor de las fundaciones Ford, Rockefeller e Interamerican; Consultor de las universidades de Stanford y Harvard (EE.UU) y profesor visitante de la Universidad de Alberta (Canadá).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.003

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 head0.063
GPT teacher head0.403
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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