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Record W4283012110 · doi:10.1344/ridas2022.13.7

Percepciones sobre el aprendizaje-servicio en profesores en formación

2022· article· es· W4283012110 on OpenAlexaff
Patricia Castillo, Katherine Acosta, Inelia Villalobos

Bibliographic record

VenueRIDAS Revista Iberoamericana de Aprendizaje y Servicio · 2022
Typearticle
Languagees
FieldSocial Sciences
TopicEducation and Teacher Training
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsHumanitiesSociologyPhilosophy

Abstract

fetched live from OpenAlex

El aprendizaje-servicio se considera como una metodología activa participativa cuyo objetivo es enriquecer el aprendizaje junto con la comunidad, promoviendo los valores cívicos, participación y responsabilidad ciudadana. El presente artículo tiene como propósito analizar las percepciones que emergen respecto al aprendizaje-servicio en 19 estudiantes pertenecientes a las carreras de Educación Parvularia y Pedagogía en Educación Básica de una universidad ubicada al norte de Chile. El campo de acción del proyecto ejecutado se sitúa en el trabajo con niños institucionalizados en una de las residencias del ex Servicio Nacional de Menores (SENAME, CHILE) durante 6 meses y en el marco de la práctica docente. Se realiza un estudio exploratorio y se suministra un cuestionario de autoinforme. Respecto a las competencias, capacidades y habilidades desarrolladas, se perciben con alto valor los comportamientos profesionales éticos que emergen de la experiencia. Paralelamente, los resultados destacan el alto valor que le otorgan las estudiantes a la formación de competencias de carácter cívico-social y los principios éticos de la profesión para la que se están preparando y que son atribuidos al compromiso social de la universidad.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.004
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.340
Teacher spread0.320 · 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 designQualitative
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

Citations2
Published2022
Admission routes1
Has abstractyes

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