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Record W4310519849 · doi:10.14738/assrj.911.13263

Discussing the Negative Impacts of Virtual Pilgrimage on Socio-Cultural & Religious Activities of Selected Asian Destinations during the Covid-19 Crisis, and the Need for Physical Presence

2022· article· en· W4310519849 on OpenAlexaff
Anthony E. Onyeama, Ergin Ersoy, Precious C. Ndukauba

Bibliographic record

VenueAdvances in Social Sciences Research Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsPilgrimagePilgrimTourismDestinationsReligious tourismGeographySustainable developmentEconomyDevelopment economicsPolitical scienceEconomicsLawArchaeology

Abstract

fetched live from OpenAlex

Pilgrimage tourism involves the physical traveling of pilgrims to a specific place of rituals. Pilgrimage as a distant journey has immense contributions to the sustainable development of tourism destinations until recently. The emergence of the Covid-19 crisis introduced some socio-cultural and religious changes through virtual pilgrimage (VP). VP is a type of pilgrimage that limits the physical presence of pilgrims to the place of rituals. Although VP reduces the spread of Covid-19 infections, it limits the sustainable development of socio-cultural benefits of biodiversity. For example, it reduces the aesthetic values of natural ecosystems and aboriginal relationships with the pilgrims. Previous studies on pilgrimage tourism have neglected the socio-cultural impacts of VP on tourism destinations during the Covid-19 crisis, preferring to focus instead on tourism development and types of pilgrimage. This emphasis could be critical if not well addressed. This study explored the negative impacts of VP on the socio-cultural dimensions of biodiversity and religious activities of selected pilgrimage destinations during the Covid-19 crisis using primary and secondary data. Findings from stage one suggested that VP introduces socio-cultural and economic changes such as a decrease in arrivals, pilgrim’s well-being, and job opportunities. Stage two analysis confirmed the findings. The study also discussed the limitations and future studies.

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)0.008
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0160.011
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.048
GPT teacher head0.451
Teacher spread0.403 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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