'Flowers Will Bloom': Assessing Support for Survivors of Japan's 3/11 Disaster through the Charity Song
Bibliographic record
Abstract
Objective: NHK, Japan’s public broadcast network, produced a theme song in March 2012 to promote public support for survivors of the 3/11 disaster of 2011. The objective of this study is to evaluate how and to what extent this charity song Hana wa Saku (“Flowers Will Bloom”) has contributed to assisting survivors of the disaster.Methods: First, the song’s characteristics are considered in two categories, (1) the way the original Japanese recording was conducted, and (2) the song itself. Then, the effects on the survivors are assessed through analyzing the actual ways the song was performed and their repercussions covered by the media.Results: (1) Along with the composer and the lyricist, all singers (thirty-six celebrities) of the original Japanese recording had strong personal links to the region affected by the disaster. In the video-clip performance, each singer relays a phrase of the song, holding a gerbera daisy instead of a baton. This performance has left a lasting impression of connectedness specifically between the survivors and the victims.(2) According to lyricist IWAI Shunji, the song’s words represent “a message from those who lost their lives to the people they left behind.” Repeatedly listening to the fictional messages of their loved ones in heaven, through the voices of the living people trying to provide comfort, have particularly helped young survivors bounce back from their sufferings.Conclusion: Responding to NHK’s call, more than 10,000 people have posted their amateur performances of the song on the official website by May 2013. All royalties and other proceeds of the song are being donated for recovery efforts for the affected region (28,907,109 yen as of March 2013). Findings demonstrate that, despite differences in its impact among survivors, this charity song can be an effective and accessible means to foster public support for the survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".