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Bibliographic record
Abstract
Dong Do Lake is an ecotourism destination exuding pristine beauty, enveloped by the lush greenery of nature. Beyond admiring the unspoiled landscapes, visitors to Dong Do Lake can engage in a multitude of captivating activities, including camping, boating, and indulging in BBQ feasts. In order to investigate the determinants of choosing Dong Do Lake ecotourism destination by Hanoi residents, the research team employed a quantitative research method based on data collected from a survey of 300 individuals, including 159 people who had previously visited Dong Do Lake for ecotourism purposes. The data was cleaned and processed using SMARTPLS software. The research results indicate that, with a 95% confidence level, the "Attitude towards the Dong Do Lake ecotourism destination" (ATT) has the strongest impact on the decision to choose Dong Do Lake ecotourism destination by Hanoi residents, with an impact level of 0.357. Next is the factor "Communication Strategy of the Dong Do Lake ecotourism destination" (CS) with an impact level of 0.249, followed by the factor "Travel Costs" (TC) with an impact level of 0.225, and the factor "Reference Group" (RG) with an impact level of 0.196. Based on the analysis results, the research team provides some discussion points to promote the decision to choose Dong Do Lake ecotourism destination for both people in general and Hanoi people in particular.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".