Modeling Different Scenarios for Forecasting Human Resources Requirements in Taiwan’s Recreational Farms
Bibliographic record
Abstract
The demands of the rural recreational market have increased in recent years. Taiwan’s rural area is also a popular travel destination for inbound tourists. Taiwan’s recreational farms are the destinations that best represent the rural recreational experience. Taiwan’s recreational agriculture association data show that Taiwan had 377 legal recreational farms in the year 2014. However, recreational farms face an area of management difficulty: how to achieve a fixed flow of human resources management. Hence, this study aimed to explore the optimal human resources in recreational farms by using system dynamics theory and modeling the financial, tourism and human resources subsystems as the decision making supports. Vensim 5.2 for Windows (Ventana Systems, Inc., 2012) was used as a research tool to test and verify two recreational farms in Taiwan as empirical cases. The results were used as the basis of the human resources requirements for recreational farm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".