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Record W4200013662 · doi:10.5206/cieeci.v50i1.10925

English for Academic Purposes Programs: Key Trends Across Canadian Universities

2021· article· en· W4200013662 on OpenAlexafffundvenueabout
Scott Roy Douglas, Michael Landry

Bibliographic record

VenueComparative and International Education · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTest of English as a Foreign LanguageDescriptive statisticsContext (archaeology)English for academic purposesKey (lock)Computer scienceMathematics educationRepresentation (politics)Academic yearMedical educationLibrary sciencePsychologyEnglish languagePolitical scienceStatisticsMathematicsGeographyMedicineComputer security

Abstract

fetched live from OpenAlex

Because of the large number of post-secondary English for academic purposes (EAP) programs and the varying ways they are structured, it can be difficult to identify how a particular program fits within the overall landscape of university education. To identify general trends across Canada, the webpages for 74 EAP programs at 50 public English-medium universities were examined for key information related to each program. Data analysis included descriptive statistics as well as graphical representation. The results pointed to typical EAP programs that are independent units that offer non-credit courses with some credit options, have international tuition fees around $9,000 per semester, provide approximately 22 hours of instruction per week, and generally require IELTS scores over 5.0 or TOEFL iBT scores over 59 for entry. These results provide an avenue of comparison and indicate the need for future research to better understand how EAP programming is conceptualized in the Canadian context.

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)0.002
metaresearch head score (Gemma)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.019
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.103
GPT teacher head0.371
Teacher spread0.268 · 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

Citations14
Published2021
Admission routes4
Has abstractyes

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Same venueComparative and International EducationSame topicSecond Language Learning and TeachingFrench-language works237,207