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Record W2810023492 · doi:10.18172/con.3335

La enseñanza de los textos deportivos. Estudio de las portadas impresas de España y Portugal

2018· article· es· W2810023492 on OpenAlexfundno aff
Sergio Suárez Ramírez, Ângela Balça, Paulo Costa

Bibliographic record

VenueContextos Educativos Revista de Educación · 2018
Typearticle
Languagees
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
FundersEuropean Regional Development FundFundação para a Ciência e a TecnologiaUniversidade do MinhoInternational Council for Canadian Studies
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

En la sociedad actual, con Internet, hay infinidad de textos. Por eso resulta necesario seleccionar los más atractivos para el alumnado con el objetivo de fomentar el gusto por la lectura. Este trabajo analiza durante una semana cada uno de los enunciados que aparecen en las portadas de los periódicos deportivos de España (As, Marca, Mundo Deportivo y Sport) y Portugal (A Bola, O Jogo y Record). Con su análisis se pretende demostrar que la mayoría de los enunciados son retóricos, y que esto ocurre en ambos países. Se ha optado por los periódicos deportivos porque despiertan interés, y resulta un tema conocido para los más jóvenes. No hay que olvidar que los textos deportivos son tan creativos como los publicitarios (Guerrero Salazar, 2007). A través de una propuesta didáctica concreta se muestra cómo trabajar con títulos tan sugerentes como “Por todo lo alto” (Marca) y “¡¡Mamma mia!! (MD) o “Aviso Amarelo” (A Bola) o “Dragão à prova de ressaca” (O Jogo) para enseñar a escribir con la expresividad y emotividad. Requisitos que toda creación literaria necesita para tener éxito, para ser atractivo para los lectores.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.358
Teacher spread0.336 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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