Exploration of Rural Micro-Renewal in the Context of AIGC and Cross-Media Integration: A Case Study of an Artistic Practice in Wupu Village
Bibliographic record
Abstract
With the ongoing implementation of the rural revitalization strategy, generative artificial intelligence content (AIGC) technology has demonstrated significant potential in the field of rural renewal. This paper, starting from a case study of artistic rural development in Wupu Village, Huzhou, Zhejiang Province, explores how AIGC and cross-media integration can drive micro-renewal in rural areas through ecological education, multi-modal experiences, and digital communication. The ecological content generated by AIGC, through rich interactive forms, can enhance participants' observation of nature and multi-sensory cognition, realizing the effective integration of "technology and education. "Multi-modal expressions that combine visual, tactile, and auditory elements create an immersive artistic experience, which can further strengthen the audience's sense of identity with rural culture. Using the design practice case of Wupu Village as a focal point, this paper extracts specific application strategies for AIGC and cross-media integration in rural micro-renewal, discussing their potential for development in future rural revitalization efforts.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".