Replicating Strategy Model Green HRM and Community-Based Tourism for Sustainable Tourism Development: Evidence in Indonesia
Bibliographic record
Abstract
The aim of this research is to create a strategy model for green HRM and community-based tourism for sustainable tourism development in Indonesia.This research was conducted qualitatively using literature studies and case studies on beaches in Jember Regency.We identified four ecotourism areas in Jember, namely Watu Ulo Beach, Papuma Beach, Payangan Beach and Paseban Beach.Then we strengthened this research by conducting interviews with experts including pokdarwis (tourism awareness groups) to get an overview of the research.The results of our research form a green HRM and community-based tourism model strategy.Analyzing the findings, after carrying out several previous stages the researcher analyzed the findings based on literature, direct observation results and interviews to form a tourism development strategy model starting from the green HRM model for sustainable tourism, community-based tourism for sustainable tourism, actors for sustainable development tourism, and strategy model for green HRM and community-based tourism for sustainable tourism in Indonesia.The research contribution is as additional literature for further research related to this topic and as a basis for decision making for the development of sustainable tourism for stakeholders both in and outside Indonesia.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".