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Record W3093679774 · doi:10.5539/ibr.v13n11p106

Potentiality of Islands Based Tourism in Bangladesh: A Qualitative View from Existing Literature

2020· article· en· W3093679774 on OpenAlexvenueno aff
Sanjoy Roy, Muhammad Abdus Salam, SM Nazrul Islam, Md. Al-Amin

Bibliographic record

VenueInternational Business Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSustainabilitySmall islandBusinessSection (typography)Economic growthRegional scienceGeographyEconomic geographyEconomicsAdvertising

Abstract

fetched live from OpenAlex

The research project is mainly developed to emphasize the development of potential areas in Bangladesh especially in the islands where the tourism sector can be incorporated to act as an income generator alongside the traditional earning opportunities. To place our thoughts of this emerging sector in island areas, we had reviewed some of the works from the earlier researchers of different regions of the world worked on different islands’ tourism developments. The result of our findings after examining those works by the researchers will provide us a clear understanding of how tourism can bring balance among environment, local community, and economy of the island areas of Bangladesh which can eventually ensure sustainability. There will be some recommendations as well as policies for enacting this new industry in the island areas in Bangladesh in the last section.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.006
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.176
GPT teacher head0.463
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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