MétaCan
Menu
Back to cohort
Record W2998304566 · doi:10.20360/langandlit29428

International Graduate Students’ Perspectives on High-Stakes English Tests and the Language Demands of Higher Education

2019· article· en· W2998304566 on OpenAlexaffvenueabout
Shakina Rajendram, Jeanne Sinclair, Elizabeth Larson

Bibliographic record

VenueLanguage and Literacy · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTest of English as a Foreign LanguageGatekeepingLanguage proficiencyThematic analysisInternationalizationLanguage assessmentHigher educationPsychologyMedical educationMathematics educationPedagogyPerceptionQualitative researchPolitical scienceSociologyMedicine

Abstract

fetched live from OpenAlex

The internationalization of higher education in Canada has given rise to the increased use of standardized English language proficiency tests as gatekeeping measures in university admission policies. However, many international students who are successful on these tests still struggle with the academic and language demands of their programs. Drawing on a thematic analysis of life story interviews with five international graduate students at a major Canadian university, this study examines students’ perceptions on the skills elicited by the IELTS and TOEFL, the language demands and pragmatic norms of their graduate program in language education, and the university’s language support programs.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.009
Scholarly communication0.0080.002
Open science0.0010.006
Research integrity0.0020.005
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 head0.010
GPT teacher head0.277
Teacher spread0.266 · 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 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

Citations9
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueLanguage and LiteracySame topicSecond Language Learning and TeachingFrench-language works237,207