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Record W2588880296

Border Crossings: How Academic, Social and Cultural Experiences Converge to Shape the International Education of German and Canadian Students on the Ontario-Baden-Wuerttemberg Exchange Program

2015· dissertation· en· W2588880296 on OpenAlexaboutno aff
Amy Magdalena Jung

Bibliographic record

VenueYorkSpace (York University) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsGermanStudy abroadInstitutionPedagogyHigher educationPsychologyMathematics educationSociologyPolitical scienceSocial scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Study abroad programs contribute significantly to a students academic, personal, and professional development; however, students are often unable to articulate how their international experiences translate into specific learning outcomes and demonstrate connections between their academic and nonacademic environments and experiences. This study drew on two strands of literature study abroad and student learning and retention in higher education - and interviewed 12 Canadian and German participants in the Ontario-Baden-Wrttemberg student exchange program. The study found that while reinforcing the importance of study abroad to students learning and development, students (I) distinguished between their academic, social, and cultural experiences; (II) recognized the interconnection of these experiences while abroad and at home; and (III) highlighted the importance of the classroom and academic institution as key sites to develop friendships, social networks, and a sense of belonging that ultimately enhanced students learning outcomes and experiences in the academic, social, and cultural contexts.

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)0.000
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 score0.583
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.052
GPT teacher head0.381
Teacher spread0.329 · 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
Published2015
Admission routes1
Has abstractyes

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