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Record W1562976168

Consideraciones generales sobre la alfabetización científica en los museos de la ciencia como espacios educativos no formales

2004· article· es· W1562976168 on OpenAlexaboutno aff
Constancio Aguirre Pérez, Ana Vázquez

Bibliographic record

VenueREEC: Revista electrónica de enseñanza de las ciencias · 2004
Typearticle
Languagees
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

En el presente trabajo hemos tratado de reflexionar sobre el papel de los Museos de la Ciencia como importantes elementos que pueden contribuir significativamente al proceso de la alfabetizacion cientifica de la sociedad, por un lado como elementos complementarios al sistema educativo, durante la educacion formal y reglada, asi como desempenar un papel fundamental en los procesos de divulgacion cientifica orientada hacia los ciudadanos en terminos generales cubriendo un papel muy importante en lo que se ha dado en denominar la educacion no formal. Intentaremos precisar una serie de terminos al respecto como Educacion formal, no formal e informal segun los distintos tipos de situaciones educativas. Intentaremos establecer una serie de funciones de la Divulgacion cientifica a las que deben atender los Museos al menos en cierta medida, para acabar estableciendo de acuerdo con el profesor Legendre y el GREM de la Universidad de Quebec una forma de utilizacion del museo con fines educativos. Por ultimo, trataremos de exponer de acuerdo con Hein una aplicacion de las teorias del aprendizaje y del conocimiento a la categorizacion de los museos. Asi, intentaremos comprender las aportaciones del constructivismo a la concepcion de los museos de la Ciencia y finalizaremos enumerando una serie de principios basicos de museologia cientifica.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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 designNot applicable
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

Citations21
Published2004
Admission routes1
Has abstractyes

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Same venueREEC: Revista electrónica de enseñanza de las cienciasSame topicMuseums and Cultural HeritageFrench-language works237,207