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Record W3129747391 · doi:10.17561/ae.v22n2.5287

Lecturas literarias para una educación por la paz: itinerarios lectores en Educación Primaria

2020· article· es· W3129747391 on OpenAlexfundno aff
Moisés Selfa i Sastre, Angélica Balça, Fernando Azevedo

Bibliographic record

VenueAula de Encuentro · 2020
Typearticle
Languagees
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaInternational Council for Canadian Studies
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

La ausencia de paz es lo que propicia la guerra. Guerras hay de muchos tipos: desde las armadas hasta las que son originadas por no resolver conflictos personales por muy menudos que estos sean. Sea como fuere, la educación para la paz abarca muchos ámbitos que el niño y joven deben aprender a lo largo de su educación obligatoria. Muchas son las maneras de trabajar la educación para la paz y el respeto hacia el prójimo y sus opiniones. Entre estas maneras está la que ofrece la lectura de textos literarios de toda tipología, en los que el componente paz puede ser abordado desde diferentes ópticas. En nuestro trabajo, propondremos la lectura de textos literarios para Educación Primaria en los que es posible trabajar la didáctica de la paz desde una óptica universal. Así, aportaremos títulos de Literatura Infantil en los que sus protagonistas encarnan valores de paz como la tolerancia, la escucha, la empatía por el otro y el perdón. Se trata de textos publicados en el siglo XXI por autores nacionales y extranjeros.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0060.010
Scholarly communication0.0140.009
Open science0.0010.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0250.007

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 head0.020
GPT teacher head0.325
Teacher spread0.304 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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