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Record W2942482573 · doi:10.5430/ijhe.v8n2p68

Internationalization Context of Arabia Higher Education

2019· article· en· W2942482573 on OpenAlexvenueno aff
Salem Al‐Agtash, L. Khadra

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationInternshipHigher educationContext (archaeology)General partnershipDimension (graph theory)Internationalization of Higher EducationPosition (finance)BusinessSpace (punctuation)GermanPolitical scienceMarketingComputer scienceInternational tradeGeography

Abstract

fetched live from OpenAlex

Internationalization in Arabia higher education space is expanding rapidly. It has taken different shapes with no systematic approach to evaluate its success and impact. By analysing patterns of mobility; trends; and forms of academic cooperation in Arabia, a framework for internationalization is introduced. The purpose is to guide efforts towards a strengthened position of higher education in the international dimension. Internationalization promotes the idea of making the university a dynamic cross-boarder educational environment. The higher education space in Arabia is analysed mainly in the internationalization perspective. The German Jordanian University is presented as an illustrative example. The objective is to draw on its experience as a benchmark for devising a workable scenario for implementing internationalization as an important dimension of higher education. The results show the importance of the derived benefits of study abroad, program cooperation, partnership, internship, and research collaboration as essential ingredients of internationalization in higher education systems.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0070.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.351
Teacher spread0.336 · 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 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

Citations12
Published2019
Admission routes1
Has abstractyes

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