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Record W2789405829 · doi:10.1080/14616688.2018.1449237

Hierarchical value map of religious tourists visiting the Vatican City/Rome

2018· article· en· W2789405829 on OpenAlexaff
Bona Kim, Seongseop Kim

Bibliographic record

VenueTourism Geographies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsTourismReligious tourismFaithTypologySalientSpiritualityValue (mathematics)SociologyReligious experienceSocial psychologyReligious studiesGeographyAnthropologyPsychologyTheologyPhilosophyArchaeology

Abstract

fetched live from OpenAlex

Constructing a hierarchical value map, a psychological structure is explored to understand how religious tourists perceive the sites they visit in terms of site attributes, the benefits religious tourists seek, and their own personal values. The sample comprised foreign tourists who visited Catholic religious sites with religious tourism as their primary purpose. Using means-end chain theory, a hierarchical value map of selected religious tourists was created. A typology comprising three prominent sets of values was discovered: religious and pious values, spiritual values, and values associated with tourism and responsibility. The most salient attribute–consequence–value (A–C–V) linkage was as follows: ‘the opportunity to explore the traditions and history of religious sites’ (A)—‘had a genuinely religious experience’ (C)—‘learned about the history of my religion’ (C)—‘enhancement of faith and spirituality’ (V).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.279
Teacher spread0.268 · 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 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

Citations56
Published2018
Admission routes1
Has abstractyes

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