FACTORS AFFECTING THE DEVELOPMENT OF FOREST TOURISM CEMARA BEACH AND ITS IMPACT ON INCOME FARMER HOUSEHOLD
Bibliographic record
Abstract
The objective of this study is to examine the factors that impact the growth of Cemara Beach Forest Tourism and its effect on the earnings of farmer households. The respondents were selected through the census method, with a total of 65 units. The approach employed for analysis was path analysis and paired t test. The study revealed that the attractiveness and tourism environment of Cemara beach forest, which includes Attractions, Amenities, Accessibility, and Auxiliary Services, had a good achievement level of 72.90%. Supporting Institutions for tourism also achieved a good criterion of 65.85%. Additionally, community participation showed good conditions with a good criteria achievement level of 61.92%. The development of Cemara Beach Forest Tourism attained a good criteria achievement level of 77.93%. Tourist Attraction and Environment had a positive correlation r = 0.89, indicating a very close relationship with Supporting Institutions. This suggests that the better the Supporting Institutions are, the better the Tourist Attraction and Environment. Community Participation demonstrated an indication of a very close relationship with Tourist Attraction and Environment. This implies that community participation increases with better tourist attractions and the environment. Supporting Institutions had a positive correlation r = 0.89 with community participation, indicating a close relationship. This implies that the better the Supporting Institutions, the better the community participation. Tourist Attraction and Environment, Supporting Institutions, and Community Participation had a positive effect on the development of Cemara beach forest tourism, with tourist attraction having the greatest influence at 46.85%, followed by Supporting Institutions at 25.82%, and Community Participation at 23.58%. The development of Cemara coastal forest tourism had a positive impact on the income of farmer households, increasing it by an average of 67.16%.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".