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Record W2569626035 · doi:10.5539/enrr.v7n1p1

Perceptions of the Quality of Nature-Based Tourism in Sundarban in Local and Foreign Visitors: A Case Study from Karamjal, Mongla

2017· article· en· W2569626035 on OpenAlexvenueno aff
Anirban Sarker, Eivin Røskaft, Ma Suza, Mohammad Nur Nobi

Bibliographic record

VenueEnvironment and Natural Resources Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationVisitor patternTourismAttractivenessSocioeconomicsGeographyInterviewWildlifePerceptionQuality (philosophy)BusinessPsychologyEcologySociology

Abstract

fetched live from OpenAlex

The recreational behaviour of visitors to Karamjal Forest Station in Sundarban, Bangladesh, was determined by interviewing 150 visitors. The majority of visitors were locals from Bangladesh (90%); however, recreational behaviour varied significantly between local and foreign visitors. More than half of the visitors reported coming to Sundarban for the first time. Most of the visitors were travelling for recreation and derived satisfaction from watching wildlife, particularly deer and crocodiles, and the beauty of the forest. Foreign visitors expressed more satisfaction with boat journeys than local visitors, while less educated visitors expressed more dissatisfaction with boat travel than highly educated visitors. To the question, ‘How would you describe the quality of the recreational benefits of nature-based tourism in Karamjal?’ most visitors answered “poor” or “very poor”. Visitor perception varied significantly by income level, and people of higher financial status were more satisfied than people of lower financial status with the recreational benefits of nature-based tourism in Karamjal.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.045
GPT teacher head0.402
Teacher spread0.357 · 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 designObservational
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

Citations6
Published2017
Admission routes1
Has abstractyes

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