Improvement of Human Resources Quality through Vocational Training in Tourism in Karimunjawa Islands (Central Java, Indonesia): A Pro-Economical Tourism Approach
Bibliographic record
Abstract
The effort to improve human resource quality is not easy to be implemented. This effort becomes more complicated to do when implemented to the group of poor community, especially in this case marginal community of small island. This research analyzes the characteristic of poor household in small island as well as the strategy of poverty eradication through the improvement of human resource quality. This is a qualitative research supported by quantitative approach. Data was collected through in-depth interview, focus group discussion, and survey. Research result indicates that the groups of traditional farmers and fishermen spread out of Karimunjawa islands who are categorized extremely poor and having limited human resource. In one side, Karimunjawa apparently has a potential to be a tourist spot. Karimunjawa inhabitants are interested to take part in economical tourism activity. This study recommends a strategy to eradicate poverty and improve human resource quality through Pro-Poor Tourism (PPT) Approach which is based on vocational tourism training.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".