Tourism Potential and How About Its Sustainability (Case Study on Sibandang Island)
Bibliographic record
Abstract
Tourism industry is susceptible to significant shocks like the COVID-19 pandemic.Many researchers have highlighted the need to conduct comprehensive studies of COVID-19 and its economic and social consequences.As a result, tackling sustainable tourism is very important because dealing with the impact of the COVID-19 pandemic is part of tackling sustainability.This study aims to analyze the potential and sustainability of tourism on Sibandang Island.This study used qualitative research methods.The research subjects in this study were elements of the local community: North Tapanuli Regency Tourism Office, Village Officials, Tourism Awareness Groups (Pokdarwis) and local communities.Overall, the tourism potential on Sibandang Island is excellent.However, stakeholders need to manage the island so that it is feasible and able to provide a quality travel experience for tourists.Furthermore, to maintain Sibandang Island, it is necessary to apply the concept of sustainable tourism so that tourism can still positively impact the community's economy, protect the environment around Sibandang Island, and preserve the existing culture.Thus, tourism on Sibandang Island can still be enjoyed by the next generation and still exists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".