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Record W4392909086 · doi:10.32920/25417327.v1

The Immigration and Settlement Experiences of International Students From Ghana and Nigeria in the Greater Toronto Area

2024· preprint· en· W4392909086 on OpenAlexafffundabout
Peter Haastrup

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan University
FundersGovernment of Canada
KeywordsImmigrationSettlement (finance)ChinaInternational educationFocus groupPolitical scienceEconomic growthGeographySociologySocioeconomicsHigher educationBusinessLaw

Abstract

fetched live from OpenAlex

Over the last 20 years, the number of international students coming to Canada has increased drastically from just over 100,000 in 2000-2001 to well over half a million in the 20202021 academic years (Canadian Bureau for International Education (CBIE), 2018; El-Assal, 2020; Statista, 2021). According to Global Affairs Canada (2020), in 2018 alone, international students in Canada contributed $22.3 billion to the Canadian economy on tuition, accommodation, and discretionary spending and helped to sustain over 170,000 jobs. Much of the literature has focused on Asian international students – especially from India and China, who make up a combined 56% of the international student body in Canada (El-Assal, 2020). Significantly less is known about African international students, who make up about 9% of the international student body in Canada. This study critically assesses Canada’s approach to and focus on international students as part of overall programs to attract immigrants. Using a qualitative analysis, this study focuses on the immigration and settlement experiences of Black African international students from Ghana and Nigeria living in the Greater Toronto Area (GTA) Canada. Semi structured interviews were conducted with Ghanaian (N = 6) and Nigerian (N = 14) international students living in the GTA. Because experiences may defer between male and female, an even number of students who identified as men (N = 10) and identified as women (N = 10) were recruited. The study centred the voices of these Ghanaian and Nigerian international students, adding to the scarce body of knowledge surrounding the topic. The research focused on the ways in which social networks contributed to or diminished acculturative stress; the impact of Canadian immigration policy on the experiences of these students; and the role of race and gender on the immigration experiences of these students. The study resulted in three key finding. First, Ghanaian and Nigerian international students face financial difficulties. They could benefit from additional forms of financial aid, grants, or financial support, although participants identified as female experienced more financial challenges than those identified as male. Second, there is a lack of culturally appropriate support from colleges and universities for Ghanaian and Nigerian international students, which have led some to seek support from their religious institutions, some of which are not equipped to help with mental wellbeing. Lastly, some participants that used educational agents, expressed discontent and frustration with the outcomes, as agencies at times gave them misinformation that led them to enroll in bridging programs when, in fact, this was not necessary. The study provides recommendations that can inform policy change to better support and enhance the experiences of Ghanaian and Nigerian international students in the GTA.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.633
Threshold uncertainty score0.729

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0170.008
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
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 head0.327
Teacher spread0.311 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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