A Case Study of Intergenerational Exchanges as an Innovation Model for the Reduction of Social Isolation and Loneliness in Multiple Generations in Japan
Bibliographic record
Abstract
Since Japan’s population has been aging at an unprecedented speed recently, the Cabinet Office of the Government of Japan set up an office to develop countermeasures against isolation and loneliness in 2021. However, while existing studies discuss various interventions for those problems, only some interventions are shown to improve feelings of social isolation, and whether or not these interventions are similarly effective across different ages is still vague. In addition, few studies discuss community-based interventions with the participation of multiple generations to deal with loneliness and social isolation. Existing studies on community farms have yet to extensively discuss their effects on social isolation and loneliness across different ages. This can neglect the potential roles of this activity as a salve to those problems, especially in countries like Japan with high rates of aging and low birthrates. Furthermore, the participation of international students in community farming is considered a rare activity and needs to be delineated beyond existing research. This article describes the SDGs11 Connect Aomori Yokouchi Project in Aomori City, Japan. The article aims to discuss its potential as an innovation model for further research and practice on reducing social isolation and loneliness in multiple generations in Japan and other countries. In this project, international and Japanese students use vacant farmland to grow flowers and vegetables as community farms. Residents of every age (very young children, elementary school students, adults, and the elderly) are eligible to participate in this activity. This activity attempts to promote exchange between generations, to regenerate and foster connections among people, and to help encourage young people to settle down and contribute something to their community. As a result, it contributes to reducing or preventing feelings of isolation and loneliness through the mutual exchange of the participants, students and older residents.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".