Cultural Policy Proposals Based on the Oral Histories of Elderly Female Artists in Incheon and Comparative International Case Studies
Bibliographic record
Abstract
This study investigates the cultural and social contributions of elderly female artists in Incheon, South Korea, through oral history and proposes actionable policy strategies to foster a sustainable cultural and artistic ecosystem. Drawing on in-depth interviews conducted between 2020 and 2024 with five artists－each with over 30 years of experience across literature, visual arts, traditional music, photography, and theatre and film－the study analyzes the intersections of artistic practice, local identity, and institutional limitations. Findings reveal that the artistic practices of these women serve not only as individual expressions but also as vital cultural assets that shape the identity and heritage of local communities. Their work transcends personal narratives, functioning as a living archive of collective memory and social cohesion. In response, the study proposes innovative policy models, including the “Community Art Maru” platform, a “Three-Stage Creative Life Model,” and a “Guaranteed Artistic Life Policy.” These frameworks emphasize the need for age- and discipline-sensitive creative education, equitable access to artistic participation, and a transparent support system led by expert review panels. By situating these insights within a comparative framework－examining cultural aging policies in the UK, Canada, and Finland－this research highlights globally applicable pathways for inclusive cultural policy development. Ultimately, this study transforms marginalized voices into foundational resources for cultural policymaking and contributes to a more equitable and resilient arts ecosystem.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".