A method for mining the association of homogenized elements of traditional crafts and cultural products based on latent semantic analysis
Bibliographic record
Abstract
Traditional craft and cultural products have become increasingly homogenized in recent years as the Internet has grown in popularity.Rural tourism has gained a lot of attention since the rural revitalization strategy was put into place, rural tourism has grown rapidly, but the problem of homogenization of rural tourism products has emerged.This paper examines the factors contributing to the homogenization of rural tourism products, as well as possible solutions, by starting with the unique traditional craft and cultural product of the region.In addition, to address the homogenization issue, this paper uses the latent semantic analysis method and the KNN method to mine the homogenization elements in the product.Because the traditional KNN text algorithm only considers simple concept matching when calculating the similarity between texts, the KNN classifier is used to classify the terms in the training and test sets.Meaning can be lost and classification results can be inaccurate as a result of this.The KNN algorithm is used in this paper to classify the semantic information of low-dimensional texts, in order to achieve the mining and classification of homogeneous elements in response to this situation.Algorithms described in this paper are capable of performing correlation mining on homogenized elements in traditional craft or cultural products, as demonstrated by the experimental results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".