Research on the Communication Opportunities of Intangible Cultural Heritage under the Background of Big Data and AI
Bibliographic record
Abstract
The study on the dissemination opportunities of Intangible Cultural Heritage (ICH) in the context of big data and artificial intelligence (AI) explores how to combine big data and AI technology to promote the inheritance and dissemination of ICH. Big data technology provides an effective way for ICH digital protection, helping to solve material loss and timeliness issues. AI technology provides a new opportunity for the digital restoration and display of ICH, which can reproduce the lost skills and cultural practices. In addition, big data and AI can also achieve personalized customization of ICH communication and improve audience participation and understanding. Most importantly, combining ICH with innovative industries will bring business opportunities for sustainable development. This paper combs the definition and importance of ICH, emphasizes the role of big data and AI in cultural communication, and emphatically analyzes the opportunities of ICH communication under the background of big data and AI, with a view to forming a new situation of ICH protection and cultural inheritance.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".