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Record W1674924700 · doi:10.18806/tesl.v17i2.891

An EAP Course for Chinese MBA Students

2000· article· en· W1674924700 on OpenAlexvenueaboutno aff
Patricia Raymond, Margaret Des Brisay

Bibliographic record

VenueTESL Canada Journal · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsEnglish for academic purposesPsychologyMedical educationTest of English as a Foreign LanguageAcculturationLanguage assessmentCourse (navigation)Graduate studentsLanguage proficiencySheltered instructionPedagogyMathematics educationLanguage educationMedicineEngineeringPolitical scienceComprehension approachImmigration

Abstract

fetched live from OpenAlex

This article describes an English for Academic Purposes (EAP) course for Chinese Master of Business Administration (MBA) students. Unequal English language learning opportunities overseas means that many otherwise excellent candidates are denied access to graduate programs at Canadian universities. Consequently, the Second Language Institute at the University of Ottawa decided to make ESL training estimates based on scores from the Canadian Test of English for Scholars and Trainees (CanTEST) for a group of Chinese applicants to the University of Ottawa's MBA program. Thirty-four candidates participated in an innovative EAP course that combined teaching language, study, and acculturation skills, whereas some candidates were also required to complete 240 to 480 hours of Intensive Four Skills English before undertaking the EAP course. Successful completion of the EAP course constituted fulfilling the requirements for admission into the MBA program. Teaching staff from both the Faculty of Administration and the Second Language Institute provided input into the EAP course.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0420.009

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 head0.009
GPT teacher head0.268
Teacher spread0.259 · 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 designNot applicable
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

Citations8
Published2000
Admission routes2
Has abstractyes

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