Research on Node Optimization and Propagation Path Calculation in Traditional Chinese Medicine Culture Propagation Networks
Bibliographic record
Abstract
In the era of digital media, with the help of media empowerment, Chinese medicine culture dissemination completes the innovation from the two dimensions of disseminators and media channels, which brings new opportunities to Chinese medicine culture dissemination.Aiming at the problem of large time overhead of traditional greedy algorithm in the optimization of nodes of TCM culture dissemination network, NPG algorithm is used to optimize the influence of starting nodes, computational efficiency and selection strategy.On the basis of optimization, the propagation probability is calculated to determine that time, content and social relationship can be used as the basis for judging the propagation path, and the path coefficients are analyzed with the help of structural equations.The path coefficient of social relationship→time→Chinese medicine culture dissemination is 0.173, i.e., under the role of time, there is a significant direct effect between social relationship and Chinese medicine culture dissemination, and time plays the role of mediating effect in the reconstruction of dissemination path.The research in this paper promotes the sustainable development of Chinese medicine culture through the improvement of Chinese medicine culture communication network.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".