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English for Specific Purposes

2018· other· en· W2903049744 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe TESOL Encyclopedia of English Language Teaching · 2018
Typeother
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlobeDisciplineEnglish for academic purposesSituatedEnglish for specific purposesMathematics educationPedagogyEnglish languageFocus (optics)PsychologyComputer scienceSociologyArtificial intelligenceSocial science

Abstract

fetched live from OpenAlex

ESP has emerged as a dominant language teaching approach across the globe for its explicit focus on why does a learner need to learn the language, and what does the learner want to do with English. The specificity of this approach centers on the needs and the instrumentality of the target linguistic proficiency in academic or occupational settings. Effective ESP instruction entails a range of curricular and pedagogical skills as well as a thorough orientation to disciplinary or occupational discourses. The classification of ESP into English for occupational purposes (or in‐service) and English for academic purposes (or pre‐service for all professions) is well‐established. ESP pedagogy is considered learner‐centered instruction, though ESP practice varies globally. In recent years, English for academic purposes has been theorized as a distinct subfield under the impact of critical and situated pedagogies in the world at large.

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.362
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.000

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 head0.013
GPT teacher head0.236
Teacher spread0.222 · 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