Bibliographic record
Abstract
The discovery of oil and gas reserves in the 1920s has made Brunei one of the world's richest countries in terms of GDP per capita. Since then, the country has been using its oil and gas revenues to finance major infrastructure projects throughout the country. Now the Sultanate's critical priority is to diversify the economy beyond its oil and gas sector. The Brunei Government has steadily opened up its economy and called for partnership with stakeholders and the private sector to take advantage of the opportunities offered by its diversification plan as outlined in the Brunei Vision 2035. The government has been looking at non-oil and gas options, and is now increasingly developing its ecotourism sector. Many have conducted ecotourism with looking into the ecotourism practice issues and impediments. Truly, Brunei ecotourism is still in its infancy and has not reached the level of development found in neighbouring areas like Sabah and Sarawak. And this is perhaps attributable to several factors, including poor understanding of ecotourism principles and lack of training. To turn Brunei into a destination for ecotourism is certainly challengeable. In this paper, the authors look into the challenges and suggest solutions turning Brunei into an ecotourism center. The authors aim to assess the recent developments of Brunei's ecotourism. And attention is particularly given to education and training intervention, a major area of interest in increasing the people's understanding of ecotourism. More importantly, the authors discuss the role of government policies and initiatives at national and local levels in the development of ecotourism.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".