Community Tourism Development in Viet Hai Commune (Cat Ba, Hai Phong City, Viet Nam) Under the Green Economy Model
Bibliographic record
Abstract
The study of community tourism development based on green economy model is the focus of this paper. The research is implemented in Viet Hai - a commune of Cat Hai island district, Hai Phong city, Vietnam. In order to evaluate, select and develop this model in the locality, a number of research methods have been applied such as: Method of seminars, expert consult, group system of sampling methods, fertilizer analyzing laboratories and methods of interviewing and collecting socio-economic data. The research outcome is given that, the community tourism based on green economy is a type of economic model that contributes not only on the local socio-economic development, but also surmounting the seasonal tourism situation, and maximizing the region's potential and strengths. The model chosen was the communitys based tourism model, with the participation of 1/3 of households in Viet Hai commune. The model has a management board with applicable rules and regulations to objects such as households, tourism operators and Viet Hai visitors. The model has been oriented towards exploiting the landscape values associated with environmental protection programs, especially towards the point-based community tourism model of Cat Ba district – which say “no” to single-use plastic products. This tourism model has created the tourism products that are different from the existing models in Cat Ba (Hai Phong), creating channels to link with tourists to attract more visitors to Viet Hai increasingly to the year.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".