MétaCan
Menu
Back to cohort
Record W2518212537 · doi:10.47678/cjhe.v46i2.184585

International Students Attending Canadian Universities: Their Experiences with Housing, Finances, and Other Issues

2016· article· en· W2518212537 on OpenAlexafffundvenueabout
Moira J. Calder, Magdalena S. Richter, Yuping Mao, Katharina Kovacs Burns, Ramadimetja Shirley Mogale, Margaret Danko

Bibliographic record

VenueCanadian Journal of Higher Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Alberta
FundersKillam Trusts
KeywordsReputationWork (physics)RevenueCurrencyHigher educationFace (sociological concept)Affordable housingPublic relationsMedical educationBusinessPolitical scienceEconomic growthSociologyFinanceEconomicsMedicineEngineering

Abstract

fetched live from OpenAlex

Universities recruit international students for a number of reasons, including enhancement of global contacts and reputation, to increase enrolment, and to generate revenue from tuition. These students face unique challenges as compared with domestic students, but no published studies or reports exist on this issue. In this article we report our findings from a survey and interviews with international graduate students, university personnel, and service providers assisting students. Students reported difficulties with finding affordable, adequate, and suitable housing; with finances, stemming from their ability to work or find employment, and from currency fluctuations; and with integration into a new university and an unfamiliar society. Administrators described limits to the assistance they could provide. Both groups suggested changes to address international students’ housing and financial issues. This study is part of a larger research project exploring housing and related issues among post-secondary students in a western Canadian city.

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)0.004
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 score0.976
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0260.007
Scholarly communication0.0080.002
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.312
Teacher spread0.297 · 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

Citations135
Published2016
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Higher EducationSame topicHigher Education Governance and DevelopmentFrench-language works237,207