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Record W4312168481 · doi:10.5539/jsd.v16n1p93

Broadening Tourism and Cultivating Sustainability: Exploring Opportunities in Bangladesh

2022· article· en· W4312168481 on OpenAlexvenueno aff
Samshad Nowreen, Sharon Moran

Bibliographic record

VenueJournal of Sustainable Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityTourismSustainable tourismLivelihoodBusinessSustainable developmentEconomic growthEnvironmental resource managementEnvironmental planningPolitical scienceEconomicsGeographyAgriculture

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.312
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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