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Record W2086739964 · doi:10.5539/elt.v8n2p1

English Language and Literature: Towards a Model for Implementation of the English/Spanish Bilingual Curriculum in Spain

2015· article· en· W2086739964 on OpenAlexvenueno aff
Jelena Bóbkina, Elena Giné Domínguez

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumGrammarPsychologyProcess (computing)Element (criminal law)Mathematics educationFace (sociological concept)LinguisticsEnglish languagePedagogyComputer science

Abstract

fetched live from OpenAlex

Bilingual Education is a must in Spain nowadays. Its implementation during the past ten years has involved a meaningful change in teaching methodology. For the first time now our students have the chance to speak and to listen to the target language before they start mastering grammar. As part of this process, English literature is being introduced as an essential element in the bilingual curriculum. Despite the expected initial difficulties that many English teachers have to face in order to work with this new curriculum, no clear reference to possible approaches or materials to be implemented have been provided in any of the existing Decrees so far. It is our intention here to present an integrative approach model for the implementation of literature in the bilingual classroom, thus providing English teachers with a powerful tool to handle literature.

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)0.010
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0100.004
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.288
Teacher spread0.269 · 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 designTheoretical or conceptual
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

Citations5
Published2015
Admission routes1
Has abstractyes

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