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Record W4390071789 · doi:10.47611/jsr.v12i3.2016

A Light at the End of the Tunnel

2023· article· en· W4390071789 on OpenAlexaff
Merab Mushfiq

Bibliographic record

VenueJournal of Student Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsAutoethnographyLonelinessOppressionAcculturationSociologyCross-cultural psychologyPsychologyPedagogyPublic relationsSocial psychologyGender studiesPolitical scienceEthnic group

Abstract

fetched live from OpenAlex

Over the last decade, North America has witnessed a tremendous growth in the enrollment of international students in higher education. Research shows that international students face various challenges and struggle to adjust in a new environment. They often experience stress, anxiety, loneliness, peer pressure, and financial issues, as well as, in some cases, forms of cultural, linguistic, and racial discrimination and inequities. This study explores the journey of an international student in North America while pursuing an undergraduate degree. Using autoethnography as a qualitative research method, I describe and systematically analyze my personal experiences as an international student. Through personal lived experiences and cultural differences, which intersect in ways that specifically speak to acculturative stress, oppression, language barriers, and academic challenges. These lived experiences and cultural differences intersect in ways that are often interconnected with social justice concerns. In conclusion, I call for providing more robust psychological, sociocultural, and academic support and services, which are crucial for international adjustment, stability and academic success. Additionally, I explain the importance of proper mentoring and peer support resources, which are necessary to navigate hurdles when transitioning into a new culture and academics, as well as in meeting everyday linguistic, social and practical challenges.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.256
GPT teacher head0.511
Teacher spread0.256 · 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.

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
Published2023
Admission routes1
Has abstractyes

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