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Record W4241647515 · doi:10.32920/ryerson.14658048

On international students in Canada : a review of their experiences in the academic literature and the Canadian media

2021· review· en· W4241647515 on OpenAlexaffabout
Sena Saidjadi

Bibliographic record

Venuenot available
Typereview
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSettlement (finance)Political scienceRevenueWork (physics)Public relationsEconomic growthBusinessEconomicsEngineering

Abstract

fetched live from OpenAlex

International students are considered active, rather than passive members of the Canadian society for a wide range of factors. First, it is important to note that the arrival of these individuals equip the country’s post-secondary education sector with an unprecedented amount of revenues in the form of tuition fees. Second, international students’ labour work and personal spending contribute towards Canada’s economic growth. Third, the presence of international students in Canada enriches the country’s socio-cultural climate. Unfortunately, international students encounter several challenges during their stay in Canada and struggle to have access to a set of comprehensive settlement services to enable them to smoothly adapt into their new environment. The following study is essentially a literature review that aims to fulfill two objectives. First, there will be an examination of the experiences and struggles of these students as they have so far been reported in the academic literature and the Canadian media. Second, some of the most prevalent knowledge gaps about international students that exist in the academic literature and the Canadian media will be identified and critically analyzed. Key Words: International Students, Language Barriers, Discrimination, Micro-Aggression, Socio-Cultural Challenges, Settlement Services, Knowledge Gaps.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.310
Teacher spread0.271 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2021
Admission routes2
Has abstractyes

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