The Role of Motivation and Creativity in Sustaining Volunteerism of Citizenship for Positive Youth Development after the Great East Japan Earthquake
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".