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Record W1542668553

Recruiting international students: A comparative study at the University of South Carolina and McGill University

2012· article· en· W1542668553 on OpenAlexaboutno aff
Megan Peryntha Patton

Bibliographic record

VenueScholar Commons (University of South Carolina) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsLibrary sciencePolitical scienceMedia studiesSociologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This research is an attempt to capture and compare best practices in international recruitment of students. The study is a reaction to policy and finance shifts in regards to international recruitment at the university level between two large, public institutions: The University of South Carolina in Columbia, SC and McGill University in Montreal, QC. For the purpose of this study, international recruitment refers to undergraduate students with citizenship outside of the host institution's country, who wish to complete a four year degree at the university. In order to understand the breadth of this trend, the study uses a comparative case study approach specifically focused on active and passive recruitment practices at each school. The external factors in regards to campus, regional, and national culture and politics are addressed as well. Both schools recognize the benefits of recruiting from abroad; the research cites many of these including increased diversity of the student body and a strong financial incentive. A major financial push at USC and a policy shift at McGill to incentivize certain majors make the study of best practices in recruitment relevant for both schools. Kolb's experiential learning theory, promoting the need for students' engagement, serves as the foundation for the motivations behind international recruitment, demonstrating the need to prepare students for a globally-affected labor market after graduation.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.001
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 head0.062
GPT teacher head0.305
Teacher spread0.243 · 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.

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
Published2012
Admission routes1
Has abstractyes

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Same venueScholar Commons (University of South Carolina)Same topicInternational Student and Expatriate ChallengesFrench-language works237,207