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Record W7048224571

International students' challenges at an English-language university in Montréal

2016· dissertation· en· W7048224571 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2016
Typedissertation
Languageen
FieldEngineering
TopicPhotocathodes and Microchannel Plates
Canadian institutionsnot available
FundersMcGill University
KeywordsStudy abroadQualitative researchInternational educationHigher educationCultural diversity
DOInot available

Abstract

fetched live from OpenAlex

The goal of this qualitative research project was to investigate different types of challenges experienced by a group of international students at an English-language university in Montral, Qubec, Canada, along with the factors which these students believed helped or could help them have a better experience.Semi-structured interviews were conducted with each of the participants.Among the experiences reported by the participants are language-related challenges, academic and university-related challenges, personal and social challenges, and cultural challenges.These are also reported in the research literature.While participants talked positively about different student services available to them on campus, they also described sources of additional support which they would consider helpful (e.g. a one-on-one language support program, financial support, etc.).This study also showed that not all international students, at the beginning of their studies, are aware of some key services available to them (i.e.peer-pairing programs, peertutoring).Given that post-secondary institutions have seen a considerable increase in international student enrolment over the past decades (The Association of Universities and Colleges of Canada, 2011Canada, , 2014;; Institute of International Education, 2014), reaching out to international students should be one of universities' priorities, so as to ensure that as many students as possible take advantage of the services they may need.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations0
Published2016
Admission routes2
Has abstractyes

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