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Record W4289826819 · doi:10.1079/tourism.2022.0025

Becoming Vincent: Using the “hero’s journey” to connect and design (digital) tourist experiences along Vincent van Gogh’s heritage locations

2022· article· en· W4289826819 on OpenAlexaboutno aff
Licia Calvi, Moniek Hover

Bibliographic record

VenueTourism Cases · 2022
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHERONarrativeStorytellingTourismVisual artsExcellenceExhibitionStudioArt historySociologyHistoryMedia studiesArtLiteratureArchaeologyLawPolitical science

Abstract

fetched live from OpenAlex

Abstract This case describes a research and design project, commissioned to Breda University of Applied Sciences (the Netherlands) by several regional tourism organizations in Brabant, a province in the south of the Netherlands. The aim of the project was to draft a narrative concept and storylines that would link and upgrade the various Vincent Van Gogh heritage sites in the area in order to eventually attract international tourists to the province, especially in view of the commemoration of Van Gogh’s 125 th death anniversary in 2015. In the creative phase, we used a 12 steps storytelling model ( Bouma, 2010 ). This is based on Campbell’s “monomyth” or “hero’s journey” (1945). It applies to many great tales and chronologically orders the (metaphorical) steps that drive the “hero” in his actions. We compared the 12 steps to Vincent’s life. The first 6 steps, which took place in Brabant, we placed under the overarching narrative concept of “Becoming Vincent”. At various locations in the province, tourists can experience how the events in Vincent’s early life led him to become the tormented yet brilliant artist so well-known from his time in France (which we defined as “Being Vincent”), thus making a full narrative circle. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © Licia Calvi & Moniek Hover 2022

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.084
GPT teacher head0.309
Teacher spread0.225 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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