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Record W3099043205

The Impact of Short-term Study Abroad on Global Citizenship Identity and Engagement

2020· article· en· W3099043205 on OpenAlexaff
Paul D. Sherman, Brianna Cofield, Neve Connolly

Bibliographic record

VenueArchivaria (Association of Canadian Archivists) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Guelph-Humber
Fundersnot available
KeywordsGlobal citizenshipCitizenshipGlobal citizenship educationProsocial behaviorStudy abroadCurriculumExperiential learningGlobal educationPedagogyIdentity (music)Public relationsPolitical scienceCitizenship educationPsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Higher education has increasingly begun to realize the importance of engaging students in global citizenship learning opportunities to be more globally informed, prepared, responsible, and competent. Study abroad in higher education is rapidly becoming recognized as an effective experiential learning platform for fostering intercultural exchanges. This article reports on research that examined study abroad as a learning platform for integrating classroom acquired knowledge with real world experience. The study explored the value of short-term study abroad in the facilitation of students' global awareness and knowledge, their identification as global citizens and endorsement of prosocial values associated with global citizenship, and their participation as globally engaged citizens. Participation in study abroad was found to significantly strengthen one’s affiliation with global citizenship, endorsement of prosocial values, and identify motivation to engage in global citizenship activities. Our findings have implications for the design and implementation of global citizenship education curricula in higher education.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0000.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.048
GPT teacher head0.371
Teacher spread0.323 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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