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Record W3164538147 · doi:10.5539/ells.v11n2p68

Assessing the Communicative Use of Literary Texts in EFL Coursebooks

2021· article· en· W3164538147 on OpenAlexvenueno aff
Shaima J. Al-Saeed, Abdullah Alenezi

Bibliographic record

VenueEnglish Language and Literature Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCommunicative competenceCommunicative language teachingMainstreamLinguisticsEnglish as a foreign languageForeign languageForeign language teachingLanguage educationCompetence (human resources)Language assessmentPsychologyComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Integrating and utilizing literary texts from coursebooks in foreign language teaching could impact the communicative competence of language learners. The study aims to scrutinize the usage of authentic and inauthentic literary texts found in 44 mainstream English as a foreign language (EFL) coursebooks. The article particularly examines how texts can facilitate communicative language learning and teaching in language classes. To do this, the study proposes a set of principles that can enable using literary texts more appropriately. The analysis of the study indicates that authentic and inauthentic literary texts are used differently, with authentic texts providing great opportunities for communication and offering a unique contribution to the EFL classroom. The study has implications for language teachers and coursebook designers in language programs. Further recommendations are made on how literature can be used communicatively.

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)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.002
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.324
Teacher spread0.278 · 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 designObservational
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

Citations4
Published2021
Admission routes1
Has abstractyes

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