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Record W2411645283 · doi:10.5430/wjel.v6n2p10

English for Academic Purposes: A New Perspective from Multiple Literacies

2016· article· en· W2411645283 on OpenAlexvenueno aff
Yulong Li, Lixun Wang

Bibliographic record

VenueWorld Journal of English Language · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsEnglish for academic purposesDisciplineLiteracyJargonPerspective (graphical)Context (archaeology)SociologyComputer scienceMathematics educationInformation literacyPedagogyLinguisticsPsychologySocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

English for Academic Purposes (EAP) has now become a popular term, due to English becoming the global languageof academia, the workplace, and higher education. At the same time, EAP has, since its creation, been influenced bythe language theories of general language teaching and literacy movements. However, its concepts and approachescan, at times, appear too various for learners and practitioners to identify which course of action to follow,particularly when the inexperienced face a ‘jungle of jargon’. EAP researchers have been trying to define EAP,however, they are never in agreement. Therefore, a definition of EAP can prove problematic. This research willextract the commonality of the popular EAP approaches before placing them into a broader context of EAPdevelopment, language teaching and literacy history, and the changing history of the educational landscape; it willcritically thematize the current EAP theories and aims in order to further examine the nature of EAP as multipleliteracies, including academic literacy, disciplinary cultural literacy, critical literacy and digital literacy.

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)0.000
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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