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Record W3097584655 · doi:10.5430/wje.v10n5p98

Swedes Studying Abroad – as Tourists?

2020· article· en· W3097584655 on OpenAlexvenueno aff
Per Anders Nilsson, Thomas Blom

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsStudy abroadPopularityDestinationsChinaDescriptive statisticsHigher educationTourismCoronavirus disease 2019 (COVID-19)GeographyAdvertisingDemographic economicsEconomic growthBusinessPolitical scienceEconomics

Abstract

fetched live from OpenAlex

The number of students temporarily studying in another country to receive a post-secondary education has grown significantly since the 1970s. This study aims to learn more about what attracts Swedish outbound students when studying abroad. What are the popular study destinations? Can data reveal touristic preferences? Descriptive statistics from the Swedish Board of Student Finance are used, making it possible to scrutinize studying abroad on an aggregate level over a period of two decades. The results show that English-speaking countries are attractive to Swedish outbound students. Places growing in popularity are the countries of Poland, Japan, the Netherlands, South Korea, Singapore, and China, as well as the region of Hong Kong in particular. However, more than 50 percent of outbound students study in Europe. In these troubling and uncertain times, we are living in, with COVID-19 and other crises hampering worldwide travel, it is difficult to predict the long-term effects on mobility.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.030
GPT teacher head0.367
Teacher spread0.337 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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