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Record W2187149880 · doi:10.82396/cjcd.v12i1.3056

International Students' Views of Transition to Employment and Immigration

2021· article· en· W2187149880 on OpenAlexaffabout
Nancy Arthur, Sarah McQueary Flynn

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImmigrationGraduation (instrument)WorkforceImmigration policyWork (physics)Political sciencePublic relationsSociologyMedical educationPedagogyMedicineLawEngineering

Abstract

fetched live from OpenAlex

This study explored international students’ views and experiences of transitioning from school to employment with the goal of permanent immigration. A semi-structured interview with critical incidents was used to assess the career transition experiences of 14 graduate international students from university to employment and permanent immigration to Canada. Data were analyzed using a constant comparison method and critical incident protocol. Despite the fact that most students had not obtained a job after completing their educational programs, the majority felt as though the decision to remain in Canada to work and eventually immigrate was a good one. Students’ expectations about better job prospects were unmet while their expectations about an enhanced quality of life in Canada were met. Students recommended that Canadian employers be more open-minded about hiring people with international experience and see the benefits of a diverse workforce. The international students hoped that those employed in career services will help future students to build networks and meet prospective employers. Students advised future international students to educate themselves about Canadian culture, how Canadians interact, and the Canadian work environment. Implications for career services and career counselling are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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

Citations36
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland)Same topicInternational Student and Expatriate ChallengesFrench-language works237,207