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Record W2767496387 · doi:10.5539/hes.v7n4p61

The Role of Motivation and Creativity in Sustaining Volunteerism of Citizenship for Positive Youth Development after the Great East Japan Earthquake

2017· article· en· W2767496387 on OpenAlexvenueno aff
Mayumi Oie

Bibliographic record

VenueHigher Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsProsocial behaviorCreativityPsychologyCitizenshipPedagogySocial psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

This paper examined how the interdisciplinary field of volunteer motivation and creativity research helps improve our understanding of social issues. This research focused on the victims of the Great East Japan Earthquake, which occurred on March 11, 2011, and discussed how volunteer motivations support volunteer activities, positive youth development and citizenship from the perspective of sociocultural and self-determination theories Next, volunteerism based on prosocial behaviors was explored, such as improvement of victims’ lives after the disaster. Despite the positive effect of volunteer activities on lifespan youth development, volunteer assistance within the stricken area has gradually declined during the past year compared to the period immediately after the disaster, when there were a substantial number of volunteers. To sustain volunteer motivation for longer periods, interdisciplinary studies within the areas of psychology and leisure are necessary. This research outlined three important interdisciplinary concepts, which are necessary to recover from the disaster: identity formation, collaborative creativity, and community citizenship. Volunteering as extracurricular activities for undergraduate and prospective teachers can strengthen their own and students’ rich and deep life course in future.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.077
GPT teacher head0.349
Teacher spread0.272 · 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.

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
Published2017
Admission routes1
Has abstractyes

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