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Record W3107928801 · doi:10.7203/leeme.46.18033

Análisis comparativo de la formación inicial del profesorado de música de primaria y secundaria en Europa

2020· article· es· W3107928801 on OpenAlexaff
Sara Domínguez-Lloria, Margarita R. Pino Juste

Bibliographic record

VenueRevista Electrónica de LEEME · 2020
Typearticle
Languagees
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsOccupational and Environmental Medical Association of Canada
Fundersnot available
KeywordsHumanitiesPhilosophyPhysics

Abstract

fetched live from OpenAlex

La formación del profesorado es, sin duda, una de las cuestiones de investigación recurrente por su importancia para el éxito del sistema educativo y su mejora se convierte en un desafío constante del ámbito educativo. La formación inicial del profesorado de música en primaria y secundaria presenta unas características muy concretas derivadas de la necesidad del conocimiento de un lenguaje específico y que es tratado de forma diferente en numerosos países europeos. El objetivo de este estudio se centra fundamentalmente en la realización de un análisis comparativo, utilizando la técnica de análisis de contenido, de tres informes de investigación sobre la formación inicial del profesorado de música de primaria y secundaria a través de una serie de categorías de análisis con el propósito de describir su organización y establecer similitudes y diferencias. Entre las principales conclusiones obtenidas observamos que las vías formativas para la formación inicial del profesorado de primaria y secundaria poseen similitudes fundamentalmente en cuanto a los centros donde se imparten y el tipo de titulación que se obtiene una vez finalizados los estudios, pero se diferencian en los requisitos previos para poder cursarlas, los años de estudios y el grado de especialización necesario para la habilitación como docente de música en ambos niveles.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.031
GPT teacher head0.292
Teacher spread0.261 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRevista Electrónica de LEEMESame topicDiverse Music Education InsightsFrench-language works237,207