Bibliographic record
Abstract
This study is intended to suggest marketing strategies for complex resort by identifying selection attributes of complex resorts from IPA as well as to identify factors of selection attributes of complex resorts recognized by users. For the study, survey was distributed to 400 complex resort users. Survey was conducted from August 1 to 31, 2019, in weekdays and weekends. Especially, survey was conducted on various age groups to well reflect opinions of complex resort users. Total 347 copies of survey were distributed, and 23 copies with incomplete answers were excluded that total 324 copies (80.1%) were used for empirical analysis. As a result of analysis, selection attributes of complex resort were derived to be seven factors of reliability, convenience, facilities, food and beverage, natural environment, employee service, and program. In addition, convenience and facilities turned out to be factors to be continuously maintained from IPA followed by natural environment as a factor to avoid excessive effort, program with low priority, and food and beverage and reliability in need of concentrated effort. According to these results, it seems that there shall various efforts to restore reliability and improve food and beverage of complex resorts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".