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Best Practices in Hosting International Students in the U.S.

2013· article· en· W1909796366 on OpenAlexvenueno aff
Murat Tas

Bibliographic record

VenueCross-cultural communication · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsChinaMedical educationScale (ratio)Higher educationInternational educationResource (disambiguation)Political scienceAdaptation (eye)Study abroadPsychologyPedagogyMedicineGeography

Abstract

fetched live from OpenAlex

The number of international students attending U.S. higher learning institutions has decreased over the past decade (excluding students from China and Saudi Arabia) from 40 percent to 30 percent. These students are an important resource for the U.S. and their native countries in terms of education, culture, and economy. Differences between international and domestic students currently enrolled in a private university were assessed using a new American College of Testing Survey of Student Opinion combined with a socio-cultural adaptation scale. Those analyzed results were incorporated into a customized 18 question survey and interview process to assess international and domestic students who had not graduated. These analyzed assessments identified and increased the understanding of needs—academic and non-academic—so that recommendations could be made to the university on how to better satisfy those needs and thereby recruit and retain more international students.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.110
GPT teacher head0.475
Teacher spread0.366 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations9
Published2013
Admission routes1
Has abstractyes

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