MétaCan
Menu
Back to cohort
Record W2922434207

Becoming Global Citizens: Learning through International Volunteering

2018· article· en· W2922434207 on OpenAlexaffabout
Akiko Ohta

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsExperiential learningGovernment (linguistics)Openness to experiencePublic relationsCitizenshipPolitical scienceGlobal citizenshipMulticulturalismImmigrationStudy abroadEconomic growthSociologyPublic administrationPedagogyPsychologySocial psychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Canada has been well known for its openness to accept new immigrants and welcome international students, and it has made Canada a multicultural country with people from diverse backgrounds. Yet, despite this focus on building multicultural coexistence, Canada has not significantly promoted to send Canadians abroad for experiential learning. Economically developed countries such as Japan, Korea, and the USA have governmental volunteering programs, which are financed by taxes, to send their own citizens abroad for two years as part of human development. On the other hand, in Canada, there is no such program and only some NGOs are hosting international volunteering. Moreover, even for those governmental programs, the main interest has been building a relationship with the local governments and cares the fact to present how much efforts the government has made for international cooperation. Little attention has been paid for volunteers’ discursive experience, learning process they go through, and how the experience has had impact on them and their life. This research sheds light on international volunteering as informal learning/life-long learning for citizenship education/global education, which lets volunteers immersed in new cultural contexts with new language(s), exposes them through the learning process, and impacts them and their life.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.323
Teacher spread0.283 · 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 teacher head, 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

Citations0
Published2018
Admission routes2
Has abstractyes

Explore more

Same venue2019 Conference of the Canadian Society for the Study of EducationSame topicTourism, Volunteerism, and DevelopmentFrench-language works237,207