Enhancing Tourist Village Quality Through Community Behavior Models
Bibliographic record
Abstract
Tourist village quality is essential in the tourism sector.Enhancing such quality can be done through community behavior models.The models consist of cognitive, affective, and psychomotor levels.This research analyzes community behavior models to enhance the quality of tourist villages in Tomok Samosir, North Sumatra.The sample included 50 tourists who came to the Tomok tourist village.Data was collected using a questionnaire after being tested.The data was analyzed using multiple regression technique.The finding accepts the hypothesis.There was a contribution between community behavior models on the tourist village quality in Tomok Samosir, North Sumatra.After tracing community behavior models, it turned out that the psychomotor model had a more substantial contribution to the quality of the tourist village in Tomok Samosir, North Sumatra, followed by the affective and cognitive models.Enhancing the quality of the Tomok Samosir North Sumatra tourist village means improving the quality of community behavior models, namely psychomotor, affective, and cognitive.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".