Becoming Vincent: Using the “hero’s journey” to connect and design (digital) tourist experiences along Vincent van Gogh’s heritage locations
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".