Broadening Tourism and Cultivating Sustainability: Exploring Opportunities in Bangladesh
Bibliographic record
Abstract
This paper explores how tourism can be understood as an opportunity to develop sustainable enterprise, providing business opportunities while also advancing social and environmental goals. While every country has multiple challenges to manage in the future, we argue that comprehensive planning for sustainable tourism can integrate several policy goals and realize compounded benefits as governments declare their commitment to ‘build back better.’ The need to plan for sustainable development is especially salient in the wake of the pandemic, and with climate change looming. Using Bangladesh as our case study, we consider how integrated and cross-sectoral planning for tourism could help provide more opportunities for visitors to appreciate the rich resources located there, such as the cultural heritage, and the rare species and mangroves of the Sundarbans, while simultaneously advancing policy goals for social welfare and the environment. We outline opportunities on the horizon, and by drawing on demographic data about the Bangladeshi diaspora, it becomes clear that heritage tourism has potential and merits further study. Finally, targeting the expansion of sustainable livelihoods can strengthen local economies and simultaneously help Bangladesh advance its efforts toward related national goals, including the UN’s SDGs (United Nations’ Sustainable Development Goals).
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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".