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Record W3171818499 · doi:10.23865/hu.v11.2344

Virtual Internationalization – we did it our way

2021· article· en· W3171818499 on OpenAlexafffundabout
Åsa Tjulin, Ellen MacEachen, Stig Vinberg, John Selander, Philip Bigelow, Robert Larsson

Bibliographic record

VenueHögre utbildning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversity of Waterloo
FundersMälardalens högskolaMittuniversitetetUniversity of Waterloo
KeywordsInternationalizationReflexivityDisseminationKnowledge managementProcess (computing)Internationalization of Higher EducationBureaucracySociologyHigher educationWork (physics)Public relationsEngineering ethicsPedagogyPolitical scienceBusinessEngineeringComputer scienceSocial science

Abstract

fetched live from OpenAlex

Using virtual internationalization as a key concept, this article adds to the body of experiencebased knowledge on how to build partnerships and develop courses within higher education. The purpose of this article is to disseminate knowledge about the collaborative process that took place when Swedish and Canadian universities created an international online course focused on work and health. The article presents the challenges and mitigating strategies during course implementation and preconditions that enabled the co-production of the course. The conclusion provides critical reflections, questions and lessons learned that arose from the instructors reflections in relation to virtual internationalization. The self-reflexive experiences were analysed through the lens of internationalization in higher education and virtual internationalization literature, and the theory of social coordination and bureaucracy to enable an understanding of how we did it our way.

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)0.008
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0140.038
Scholarly communication0.0170.023
Open science0.0020.022
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0060.001

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 head0.050
GPT teacher head0.360
Teacher spread0.310 · 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

Citations10
Published2021
Admission routes3
Has abstractyes

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