Research on strategic research of red tourism culture platform construction and brand development based on big data
Bibliographic record
Abstract
The red spirit is an important part of the Chinese culture that needs to be inherited and developed, but it may also face the problem of insufficient attraction to the general masses.This study is based on big data model, visualization model and risk model, geographical location, historical culture, policy support, population and red culture platform for independence of variables, risk degree and popularity, the actual number of visitors for the red tourism culture platform construction and brand development, at the same time with the visual model of the dynamic red tourism experience application, dynamic real-time monitoring attractions red platform and brand construction, realize the red tourism culture platform construction and brand development strategy research dynamic visualization development and provide effective decisions.In the model design, the measures taken by the experimental group have a significant effect on improving the visibility of scenic spots, and the risk degree is significantly lower than that of the control group, which indicates that the experimental group is significantly better than the control group in reducing the risk degree, improving the visibility of scenic spots and increasing the actual number of tourists.This further shows that the joint use of big data analysis model and risk assessment model has effectively promoted the construction of red tourism culture platform and brand development.At the same time, compared with the people of different age groups, the satisfaction of red scenic spots soared, reaching more than 98%.Therefore, this model plays a key role in the strategic decision-making of red tourism culture platform construction and brand development, and also provides new ideas and methods for the inheritance and protection of red culture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".