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Record W4387879089 · doi:10.1080/13676261.2023.2271869

Dreaming the life: international students and the temporal complexity of employability

2023· article· en· W4387879089 on OpenAlexafffundabout
Alison Taylor, Catalina Bobadilla Sandoval, Sameena Jamal

Bibliographic record

VenueJournal of Youth Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEmployabilityAcculturationWork (physics)SociologyPsychologyJigsawMathematics educationComputer sciencePedagogyEngineeringEthnic group

Abstract

fetched live from OpenAlex

Undergraduates today are required to make strategic decisions about where and what they study, and how they allocate time to studies, extra-curricular activities, and work.International students are likely to experience the pressure to capitalize on their time more intensely because of the high cost of their degree, the work required to acculturate, and the uncertainty of mobility aspirations.Drawing on multi-modal, longitudinal data from a small, diverse group of working international undergraduates at a Canadian university, this article examines how they respond to pressures to be planful, employable and productive.We find that students' lived experiences contradict idealized discourses of youth mobility.While they feel pressure to make the most of opportunities, the cost of intensive work and constraints on choices are apparent.The effort required to acculturate makes it difficult for such students to be efficient in their use of time.Finally, attempting to keep mobility options open in multiple sites that are constantly changing requires that they invest a significant amount of time and energy.Socio-demographic differences and mobility histories influence students' ability to choose and their experiences of term-time work, as well as their ideas about what constitutes an appreciable life.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.108
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.199
GPT teacher head0.431
Teacher spread0.232 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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