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Record W171358688 · doi:10.5206/cie-eci.v43i2.9253

A Study of the First Year International Students at a Canadian University: Challenges and Experiences with Social Integration

2014· article· en· W171358688 on OpenAlexafffundvenueabout
George Zhou, Zuochen Zhang

Bibliographic record

VenueComparative and International Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsSocializationFocus groupSocial integrationHigher educationPsychologyProcess (computing)Study abroadInternational educationPolitical sciencePedagogySociologyMedical educationPublic relationsSocial psychologyMedicine

Abstract

fetched live from OpenAlex

An increasing number of international students come to Canada for their higher education. As a unique group on Canadian campuses, international students deserve our attention so that we can understand their special needs. Using Tinto’s retention model as a theoretical framework, this study investigates the experiences of the first year international students at a Canadian university. It pays special attention to the challenges these students face in the process of their social integration into the new learning and living environment. Data were collected through surveys and focus groups. Data analysis reveals a comprehensive picture of international students’ socialization patterns and challenges. Since student retention has been a central concern for many universities, the findings of this study are informative for higher education institutions to optimize their services to meet international students’ preferences and needs.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.156
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0400.011
Scholarly communication0.0100.003
Open science0.0030.008
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.082
GPT teacher head0.370
Teacher spread0.288 · 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

Citations101
Published2014
Admission routes4
Has abstractyes

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