Exploring the Educational Potential of Immersive New Media Art in Urban Commercial Spaces in Dalian City, Liaoning Province, China
Bibliographic record
Abstract
Contemporary Chinese art has witnessed a transformative evolution in the realm of installation art, particularly in the context of immersive new media installations within urban commercial spaces. This study explores the educational potential of immersive new media art in urban commercial spaces in Dalian City, Liaoning Province, China. The research journey spans various stages of Dalian’s installation art development, from its early emergence in the 1980s to the prosperous period in the 1990s and its diversification in the 21st century. Key themes explored include the evolution of artistic expression, the impact on education, and the integration of new media in urban commercial spaces. The research site primarily focuses on Dalian’s cultural and commercial landscapes, with a spotlight on the Xiongdong Street project. The study employs a qualitative research methodology, combining field research, interviews, multimedia elements, and data analysis to offer comprehensive insights into the subject matter. Key informants include artists, scholars, and experts within the field of installation art. The research results reveal the dynamic evolution of Dalian’s installation art, its educational significance, and its integration into urban commercial spaces. The study suggests that immersive new media art has the potential to enrich art education, engage the public, and foster cultural and commercial development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".