Research Trends of Picture Books for Older Adults over 65 in South Korea
Bibliographic record
Abstract
The goal of this study is to analyze trends of picture book research for older adults over 65 during last 15 years (2005-2019) to foresee research direction in the future. Forty-three domestic studies using picture books for older adults over 65 were selected from RISS with four types of academic data including academic journal articles, masters’ and doctoral dissertations, books, and financially funded public research reports. Picture book research of older adults over 65 steadily increased with a sharp upward slope toward the 3rd term (2013-2015) reflecting the aging society of South. Korea. Research subjects were mostly focused on older adults as characters/real people in books, illiterate, and dementia as well. Psycho-emotional therapy, self-related ingredients such as self-growth, -integration, -identity, -esteem, -confidence, and young children’s understanding of older adults Diverse research methods were used with literature reviews the most. The research frequency of academic fields are in the order of, education (especially early childhood & special ED), counselling and psychotherapy, and social science. More research for older adults over 65 are needed to improve their better life and easier adaptation to aging Korea through publications, psycho-emotional support systems, and publicly financed network and research reports for practical advocacy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".