텍스트마이닝을 활용한 지역축제 활성화 방안 연구 - 진주남강유등축제를 중심으로 -
Bibliographic record
Abstract
The Jinju Namgang Lantern Festival, which has been selected as an honorary representative festival of Korea by the Ministry of Culture, Sports and Tourism since 2014, is a luxury global festival exported to Canada and the United States. The Jinju Namgang Lantern Festival, which has been handed down along with the history of the Jinju battle among the three major battles of the Japanese Invasion of Korea in 1592, has been steadily gaining popularity among tourists for its colorful exhibitions and hands-on events. This study was conducted on the Jinju Namgang Lantern Festival by applying the text mining analysis technique currently used in tourism and festival research. As a result of the study, first, in addition to festival-related regions and places, words such as fireworks, Gaecheon Arts Festival, travel, event, history, fall, Korea, place, and hope showed a high frequency of occurrence. Second, as a result of centrality analysis, travel, events, and parking showed high degree centrality, while homepage, time, opening, and region showed high closeness centrality. Third, as a result of CONCOR analysis, a total of four clusters highly related to the Jinju Namgang Lantern Festival were formed.
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 | 0.000 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".